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JCU PoC · Active Commercial site

GridMind Commercial — Founders Overview

All commercial tools, reference data, and deployment planning in one place. Use the sidebar to navigate between tools.

Platform stage
Phase 1
JCU PoC active
DTA deadline
1 Jul 2026
Cloud Marketplace app
Target ARR (1,000 nodes)
$2.32M
Blended rate model
AU AI gap by 2028
700MW–1.7GW
M3 Property Nov 2025

Quick access tools

Phase 1 milestones

JCU Ideas Lab PoC contractIn progress
DTA Cloud Marketplace applicationDue 1 Jul 2026
AusTender registrationPending
NVIDIA Australia partnershipApproach Q3 2026
MIST Ergon testing — Tier 1Phase 1 months 2–4
IRAP assessment — PROTECTEDPhase 2 months 7–18
AEMO VPP registrationPhase 2 months 7–18
Offshore leak: An estimated 87% of Australian enterprise AI inference currently routes through US-owned hyperscalers (AWS Sydney, Azure) — both subject to US CLOUD Act subpoena regardless of physical location. GridMind is the only provider with deployable sovereign AI hardware on Australian soil across all tiers.

Hardware Requirements Calculator

Define your workload and we calculate the exact hardware tier, rack count, MCPU requirement, and total cost. Based on the 5-step demand calculation framework from the GridMind Commercial Whitepaper.

Step 1 — Workload type

Step 2 — Scale and usage

Recommended configuration

GridMind Starter
2 nodes
4× RTX 4090 per node · 8× GPUs total
Hardware tierStarter (RTX 4090)
Nodes required2
Total GPUs8
Total VRAM192 GB
Total power draw3.2 kW
MCPU requiredNone
Enclosure typeIP55 outdoor unit × 2
IRAP compliantStandard tier

Hardware cost

Hardware cost (est.)$40,000
MCPU / coolingIncluded
Total installed cost$40,000
Electricity/month (QLD)-$529/mo

Value prop A — GSOL idle revenue

Idle hours/day (24 minus peak hours)16 hrs
GSOL gross revenue/month-
Platform fee (15%)-
Electricity cost-
Net idle income/month-
Payback period (idle revenue)-

Value prop B — savings vs hyperscaler

AWS equivalent rate$8.00-$10.60/GPU-hr
Current AWS monthly spend (est.)-
GridMind electricity only-
Monthly saving vs AWS-
Payback period (savings only)-
3-year ROI vs staying on AWS-

Why this configuration

Select your workload parameters to see the recommendation rationale.

Virtual Rack Builder

Visually build your rack configuration. Select a node tier and see the exact rack unit layout inside the MCPU enclosure. Drag the floor plan to see placement.

Starter unit
Starter · SMB
UNIT-A1
UNIT-A1 · Starter Plus
UNIT-A2
UNIT-A2 · Enterprise
Cluster
Cluster · Sovereign
Starter (4× RTX 4090)
Starter Plus (8× RTX 4090)
Enterprise (H100/H200)
Enterprise
Enterprise Plus H200
Enterprise Plus B200
Sovereign NVL72
Rack unit diagram
Legend
Enclosure floor plan

Idle Capacity Revenue Calculator

During hours when your primary AI workloads are not running, your hardware is idle. GSOL automatically sells that idle capacity on the GridMind marketplace — generating passive revenue for your organisation. Calculate your idle revenue potential here.

Your hardware

Your usage pattern

Idle revenue potential

Idle hours/day
16 hrs
Idle hours/year
4,340 hrs
Monthly idle revenue
$1,240
Annual idle revenue
$14,880
Your primary AI use (33%)Operating cost
GSOL idle revenue (67%)$1,240/mo
Total GPU-hours available/year
GMV generated (full year)
GridMind platform fee
Your net idle income/year

How GSOL manages idle capacity

GSOL (GridMind Sovereign Orchestration Layer) monitors your hardware continuously. When your primary workloads finish or drop below 20% GPU utilisation, GSOL automatically offers the remaining capacity to the GridMind marketplace.

Priority rules you define: You can set minimum response time guarantees (your jobs always preempt marketplace jobs within 30 seconds), working-hours lockout (no marketplace jobs 9am–5pm on weekdays), and minimum batch size (no jobs under X GPU-hours).

Revenue appears in your GSOL dashboard and is settled monthly via PayID direct to your nominated account. Fully automated — no manual marketplace management required.

Unit Economics — Queensland

GSOL idle-revenue economics for every GridMind module. 18 idle hrs/day assumed (6 hrs peak use). Electricity at $0.35/kWh (Energex standard tariff), idle-hours share only. GridMind platform fee 15%. Rates: RTX 4090 $0.53–$1.53/GPU-hr · RTX PRO 6000 $0.75–$2.00 · H100 NVL $2.00–$5.00 · H200/B200 SXM5 $2.50–$10.00/GPU-hr.

📐 How these numbers are calculated — methodology & rate sources

Spark — GB10 Grace Blackwell · 128 GB unified

Installed cost
$9,000
Op. hours/day
10 hrs
Idle hrs/day
10 hrs
Electricity/mo (20 hrs)
$94
💰 Stream 1 — Cost saving (10 hrs/day)
Owner uses their own GPU instead of paying AWS/Azure. Every operational hour saves the market rate — no GridMind fee applies.
📡 Stream 2 — GSOL idle revenue (10 hrs/day)
Unused GPU capacity sold on the GSOL marketplace. GridMind takes 15% platform fee. 4 hrs/day held as buffer.
Scenario summary — Base / Medium / Best
📊 Base case
$0.53/GPU-hr
Cost saving/mo +$159
Idle revenue/mo +$135
Electricity/mo −$94
Combined net/mo $200
Annual benefit $2,400
Payback 3.8 yrs
📈 Medium case
$1.03/GPU-hr
Cost saving/mo +$309
Idle revenue/mo +$263
Electricity/mo −$94
Combined net/mo $478
Annual benefit $5,736
Payback 1.6 yrs
🚀 Best case
$1.28/GPU-hr
Cost saving/mo +$384
Idle revenue/mo +$326
Electricity/mo −$94
Combined net/mo $616
Annual benefit $7,392
Payback 1.2 yrs
Full rate breakdown — all market tiers
Rate/GPU-hr Cost saving/mo GSOL gross/mo GridMind 15% GSOL net/mo Electricity Combined net/mo Payback
$0.53 +$159 +$159 −$24 +$135 −$94 $200 3.8 yrs
$0.78 +$234 +$234 −$35 +$199 −$94 $339 2.2 yrs
$1.03 +$309 +$309 −$46 +$263 −$94 $478 1.6 yrs
$1.28 +$384 +$384 −$58 +$326 −$94 $616 1.2 yrs
Cost saving = 1 GPU × rate × 10 op hrs × 30 days (no fee — own use). GSOL gross = 1 GPU × rate × 10 idle hrs × 30 days × 85%. Electricity = 0.45 kW × 20 hrs × 30 days × $0.35. 4 hrs/day buffer not counted.

Starter — 4× RTX 4090 · 96 GB VRAM

Installed cost
$20,000
Op. hours/day
10 hrs
Idle hrs/day
10 hrs
Electricity/mo (20 hrs)
$441
💰 Stream 1 — Cost saving (10 hrs/day)
Owner uses their own GPU instead of paying AWS/Azure. Every operational hour saves the market rate — no GridMind fee applies.
📡 Stream 2 — GSOL idle revenue (10 hrs/day)
Unused GPU capacity sold on the GSOL marketplace. GridMind takes 15% platform fee. 4 hrs/day held as buffer.
Scenario summary — Base / Medium / Best
📊 Base case
$0.53/GPU-hr
Cost saving/mo +$636
Idle revenue/mo +$541
Electricity/mo −$441
Combined net/mo $736
Annual benefit $8,832
Payback 2.3 yrs
📈 Medium case
$1.03/GPU-hr
Cost saving/mo +$1,236
Idle revenue/mo +$1,051
Electricity/mo −$441
Combined net/mo $1,846
Annual benefit $22,152
Payback 10.8 mo
🚀 Best case
$1.53/GPU-hr
Cost saving/mo +$1,836
Idle revenue/mo +$1,561
Electricity/mo −$441
Combined net/mo $2,956
Annual benefit $35,472
Payback 6.8 mo
Full rate breakdown — all market tiers
Rate/GPU-hr Cost saving/mo GSOL gross/mo GridMind 15% GSOL net/mo Electricity Combined net/mo Payback
$0.53 +$636 +$636 −$95 +$541 −$441 $736 2.3 yrs
$0.78 +$936 +$936 −$140 +$796 −$441 $1,291 1.3 yrs
$1.03 +$1,236 +$1,236 −$185 +$1,051 −$441 $1,846 10.8 mo
$1.28 +$1,536 +$1,536 −$230 +$1,306 −$441 $2,401 8.3 mo
$1.53 +$1,836 +$1,836 −$275 +$1,561 −$441 $2,956 6.8 mo
Cost saving = 4 GPUs × rate × 10 op hrs × 30 days (no fee — own use). GSOL gross = 4 GPUs × rate × 10 idle hrs × 30 days × 85%. Electricity = 2.1 kW × 20 hrs × 30 days × $0.35. 4 hrs/day buffer not counted.

Starter Plus — 8× RTX 4090 · 192 GB VRAM

Installed cost
$37,000
Op. hours/day
10 hrs
Idle hrs/day
10 hrs
Electricity/mo (20 hrs)
$861
💰 Stream 1 — Cost saving (10 hrs/day)
Owner uses their own GPU instead of paying AWS/Azure. Every operational hour saves the market rate — no GridMind fee applies.
📡 Stream 2 — GSOL idle revenue (10 hrs/day)
Unused GPU capacity sold on the GSOL marketplace. GridMind takes 15% platform fee. 4 hrs/day held as buffer.
Scenario summary — Base / Medium / Best
📊 Base case
$0.53/GPU-hr
Cost saving/mo +$1,272
Idle revenue/mo +$1,081
Electricity/mo −$861
Combined net/mo $1,492
Annual benefit $17,904
Payback 2.1 yrs
📈 Medium case
$1.03/GPU-hr
Cost saving/mo +$2,472
Idle revenue/mo +$2,101
Electricity/mo −$861
Combined net/mo $3,712
Annual benefit $44,544
Payback 10.0 mo
🚀 Best case
$1.53/GPU-hr
Cost saving/mo +$3,672
Idle revenue/mo +$3,121
Electricity/mo −$861
Combined net/mo $5,932
Annual benefit $71,184
Payback 6.2 mo
Full rate breakdown — all market tiers
Rate/GPU-hr Cost saving/mo GSOL gross/mo GridMind 15% GSOL net/mo Electricity Combined net/mo Payback
$0.53 +$1,272 +$1,272 −$191 +$1,081 −$861 $1,492 2.1 yrs
$0.78 +$1,872 +$1,872 −$281 +$1,591 −$861 $2,602 1.2 yrs
$1.03 +$2,472 +$2,472 −$371 +$2,101 −$861 $3,712 10.0 mo
$1.28 +$3,072 +$3,072 −$461 +$2,611 −$861 $4,822 7.7 mo
$1.53 +$3,672 +$3,672 −$551 +$3,121 −$861 $5,932 6.2 mo
Cost saving = 8 GPUs × rate × 10 op hrs × 30 days (no fee — own use). GSOL gross = 8 GPUs × rate × 10 idle hrs × 30 days × 85%. Electricity = 4.1 kW × 20 hrs × 30 days × $0.35. 4 hrs/day buffer not counted.

Pro — 4× RTX PRO 6000 Server Ed. · 192 GB ECC

Installed cost
$78,000
Op. hours/day
10 hrs
Idle hrs/day
10 hrs
Electricity/mo (20 hrs)
$504
💰 Stream 1 — Cost saving (10 hrs/day)
Owner uses their own GPU instead of paying AWS/Azure. Every operational hour saves the market rate — no GridMind fee applies.
📡 Stream 2 — GSOL idle revenue (10 hrs/day)
Unused GPU capacity sold on the GSOL marketplace. GridMind takes 15% platform fee. 4 hrs/day held as buffer.
Scenario summary — Base / Medium / Best
📊 Base case
$0.75/GPU-hr
Cost saving/mo +$900
Idle revenue/mo +$765
Electricity/mo −$504
Combined net/mo $1,161
Annual benefit $13,932
Payback 5.6 yrs
📈 Medium case
$1.50/GPU-hr
Cost saving/mo +$1,800
Idle revenue/mo +$1,530
Electricity/mo −$504
Combined net/mo $2,826
Annual benefit $33,912
Payback 2.3 yrs
🚀 Best case
$2.00/GPU-hr
Cost saving/mo +$2,400
Idle revenue/mo +$2,040
Electricity/mo −$504
Combined net/mo $3,936
Annual benefit $47,232
Payback 1.7 yrs
Full rate breakdown — all market tiers
Rate/GPU-hr Cost saving/mo GSOL gross/mo GridMind 15% GSOL net/mo Electricity Combined net/mo Payback
$0.75 +$900 +$900 −$135 +$765 −$504 $1,161 5.6 yrs
$1.00 +$1,200 +$1,200 −$180 +$1,020 −$504 $1,716 3.8 yrs
$1.50 +$1,800 +$1,800 −$270 +$1,530 −$504 $2,826 2.3 yrs
$2.00 +$2,400 +$2,400 −$360 +$2,040 −$504 $3,936 1.7 yrs
Cost saving = 4 GPUs × rate × 10 op hrs × 30 days (no fee — own use). GSOL gross = 4 GPUs × rate × 10 idle hrs × 30 days × 85%. Electricity = 2.4 kW × 20 hrs × 30 days × $0.35. 4 hrs/day buffer not counted.

Pro Plus — 8× RTX PRO 6000 Server Ed. · 384 GB ECC

Installed cost
$129,000
Op. hours/day
10 hrs
Idle hrs/day
10 hrs
Electricity/mo (20 hrs)
$1,008
💰 Stream 1 — Cost saving (10 hrs/day)
Owner uses their own GPU instead of paying AWS/Azure. Every operational hour saves the market rate — no GridMind fee applies.
📡 Stream 2 — GSOL idle revenue (10 hrs/day)
Unused GPU capacity sold on the GSOL marketplace. GridMind takes 15% platform fee. 4 hrs/day held as buffer.
Scenario summary — Base / Medium / Best
📊 Base case
$0.75/GPU-hr
Cost saving/mo +$1,800
Idle revenue/mo +$1,530
Electricity/mo −$1,008
Combined net/mo $2,322
Annual benefit $27,864
Payback 4.6 yrs
📈 Medium case
$1.50/GPU-hr
Cost saving/mo +$3,600
Idle revenue/mo +$3,060
Electricity/mo −$1,008
Combined net/mo $5,652
Annual benefit $67,824
Payback 1.9 yrs
🚀 Best case
$2.00/GPU-hr
Cost saving/mo +$4,800
Idle revenue/mo +$4,080
Electricity/mo −$1,008
Combined net/mo $7,872
Annual benefit $94,464
Payback 1.4 yrs
Full rate breakdown — all market tiers
Rate/GPU-hr Cost saving/mo GSOL gross/mo GridMind 15% GSOL net/mo Electricity Combined net/mo Payback
$0.75 +$1,800 +$1,800 −$270 +$1,530 −$1,008 $2,322 4.6 yrs
$1.00 +$2,400 +$2,400 −$360 +$2,040 −$1,008 $3,432 3.1 yrs
$1.50 +$3,600 +$3,600 −$540 +$3,060 −$1,008 $5,652 1.9 yrs
$2.00 +$4,800 +$4,800 −$720 +$4,080 −$1,008 $7,872 1.4 yrs
Cost saving = 8 GPUs × rate × 10 op hrs × 30 days (no fee — own use). GSOL gross = 8 GPUs × rate × 10 idle hrs × 30 days × 85%. Electricity = 4.8 kW × 20 hrs × 30 days × $0.35. 4 hrs/day buffer not counted.

Enterprise — 8× H100 NVL · 640 GB HBM2e

Installed cost
$543,000
Op. hours/day
10 hrs
Idle hrs/day
10 hrs
Electricity/mo (20 hrs)
$966
💰 Stream 1 — Cost saving (10 hrs/day)
Owner uses their own GPU instead of paying AWS/Azure. Every operational hour saves the market rate — no GridMind fee applies.
📡 Stream 2 — GSOL idle revenue (10 hrs/day)
Unused GPU capacity sold on the GSOL marketplace. GridMind takes 15% platform fee. 4 hrs/day held as buffer.
Scenario summary — Base / Medium / Best
📊 Base case
$2.00/GPU-hr
Cost saving/mo +$4,800
Idle revenue/mo +$4,080
Electricity/mo −$966
Combined net/mo $7,914
Annual benefit $94,968
Payback 5.7 yrs
📈 Medium case
$3.00/GPU-hr
Cost saving/mo +$7,200
Idle revenue/mo +$6,120
Electricity/mo −$966
Combined net/mo $12,354
Annual benefit $148,248
Payback 3.7 yrs
🚀 Best case
$5.00/GPU-hr
Cost saving/mo +$12,000
Idle revenue/mo +$10,200
Electricity/mo −$966
Combined net/mo $21,234
Annual benefit $254,808
Payback 2.1 yrs
Full rate breakdown — all market tiers
Rate/GPU-hr Cost saving/mo GSOL gross/mo GridMind 15% GSOL net/mo Electricity Combined net/mo Payback
$2.00 +$4,800 +$4,800 −$720 +$4,080 −$966 $7,914 5.7 yrs
$3.00 +$7,200 +$7,200 −$1,080 +$6,120 −$966 $12,354 3.7 yrs
$4.00 +$9,600 +$9,600 −$1,440 +$8,160 −$966 $16,794 2.7 yrs
$5.00 +$12,000 +$12,000 −$1,800 +$10,200 −$966 $21,234 2.1 yrs
Cost saving = 8 GPUs × rate × 10 op hrs × 30 days (no fee — own use). GSOL gross = 8 GPUs × rate × 10 idle hrs × 30 days × 85%. Electricity = 4.6 kW × 20 hrs × 30 days × $0.35. 4 hrs/day buffer not counted.

Enterprise Plus H200 — 8× H200 SXM5 · 1.1 TB HBM3e

Installed cost
$898,000
Op. hours/day
10 hrs
Idle hrs/day
10 hrs
Electricity/mo (20 hrs)
$1,764
💰 Stream 1 — Cost saving (10 hrs/day)
Owner uses their own GPU instead of paying AWS/Azure. Every operational hour saves the market rate — no GridMind fee applies.
📡 Stream 2 — GSOL idle revenue (10 hrs/day)
Unused GPU capacity sold on the GSOL marketplace. GridMind takes 15% platform fee. 4 hrs/day held as buffer.
Scenario summary — Base / Medium / Best
📊 Base case
$2.50/GPU-hr
Cost saving/mo +$6,000
Idle revenue/mo +$5,100
Electricity/mo −$1,764
Combined net/mo $9,336
Annual benefit $112,032
Payback 8.0 yrs
📈 Medium case
$4.00/GPU-hr
Cost saving/mo +$9,600
Idle revenue/mo +$8,160
Electricity/mo −$1,764
Combined net/mo $15,996
Annual benefit $191,952
Payback 4.7 yrs
🚀 Best case
$8.00/GPU-hr
Cost saving/mo +$19,200
Idle revenue/mo +$16,320
Electricity/mo −$1,764
Combined net/mo $33,756
Annual benefit $405,072
Payback 2.2 yrs
Full rate breakdown — all market tiers
Rate/GPU-hr Cost saving/mo GSOL gross/mo GridMind 15% GSOL net/mo Electricity Combined net/mo Payback
$2.50 +$6,000 +$6,000 −$900 +$5,100 −$1,764 $9,336 8.0 yrs
$4.00 +$9,600 +$9,600 −$1,440 +$8,160 −$1,764 $15,996 4.7 yrs
$6.00 +$14,400 +$14,400 −$2,160 +$12,240 −$1,764 $24,876 3.0 yrs
$8.00 +$19,200 +$19,200 −$2,880 +$16,320 −$1,764 $33,756 2.2 yrs
Cost saving = 8 GPUs × rate × 10 op hrs × 30 days (no fee — own use). GSOL gross = 8 GPUs × rate × 10 idle hrs × 30 days × 85%. Electricity = 8.4 kW × 20 hrs × 30 days × $0.35. 4 hrs/day buffer not counted.

Enterprise Plus B200 — 8× B200 Blackwell · 1.5 TB HBM3e

Installed cost
$1.36M
Op. hours/day
10 hrs
Idle hrs/day
10 hrs
Electricity/mo (20 hrs)
$2,037
💰 Stream 1 — Cost saving (10 hrs/day)
Owner uses their own GPU instead of paying AWS/Azure. Every operational hour saves the market rate — no GridMind fee applies.
📡 Stream 2 — GSOL idle revenue (10 hrs/day)
Unused GPU capacity sold on the GSOL marketplace. GridMind takes 15% platform fee. 4 hrs/day held as buffer.
Scenario summary — Base / Medium / Best
📊 Base case
$3.00/GPU-hr
Cost saving/mo +$7,200
Idle revenue/mo +$6,120
Electricity/mo −$2,037
Combined net/mo $11,283
Annual benefit $135,396
Payback 10.0 yrs
📈 Medium case
$5.00/GPU-hr
Cost saving/mo +$12,000
Idle revenue/mo +$10,200
Electricity/mo −$2,037
Combined net/mo $20,163
Annual benefit $241,956
Payback 5.6 yrs
🚀 Best case
$10.00/GPU-hr
Cost saving/mo +$24,000
Idle revenue/mo +$20,400
Electricity/mo −$2,037
Combined net/mo $42,363
Annual benefit $508,356
Payback 2.7 yrs
Full rate breakdown — all market tiers
Rate/GPU-hr Cost saving/mo GSOL gross/mo GridMind 15% GSOL net/mo Electricity Combined net/mo Payback
$3.00 +$7,200 +$7,200 −$1,080 +$6,120 −$2,037 $11,283 10.0 yrs
$5.00 +$12,000 +$12,000 −$1,800 +$10,200 −$2,037 $20,163 5.6 yrs
$7.00 +$16,800 +$16,800 −$2,520 +$14,280 −$2,037 $29,043 3.9 yrs
$10.00 +$24,000 +$24,000 −$3,600 +$20,400 −$2,037 $42,363 2.7 yrs
Cost saving = 8 GPUs × rate × 10 op hrs × 30 days (no fee — own use). GSOL gross = 8 GPUs × rate × 10 idle hrs × 30 days × 85%. Electricity = 9.7 kW × 20 hrs × 30 days × $0.35. 4 hrs/day buffer not counted.

MCPU — Modular Cooling & Power Unit

The GridMind MCPU is the external cooling and power distribution module for Enterprise-tier nodes. It handles liquid cooling (direct liquid cooling, dry coolers, or full fluid pods) and serves as the primary interface between the compute pod and site utilities.

MCPU-S — Small
Up to 15 kW cooling · Outdoor dry cooler wall-mount · Suits Starter Plus, Pro, Pro Plus, Enterprise H100 NVL
~$8,000–$14,000 installed
MCPU-M — Medium
Up to 60 kW cooling · Dry cooler array 2–4 panels · Suits Enterprise H100 SXM5, H200, B200
~$35,000–$65,000 installed
MCPU-L — Large
Up to 140 kW cooling · Full outdoor fluid pod · Suits Sovereign NVL72, multi-pod clusters
~$120,000–$200,000 installed
What the MCPU does: GPU compute generates intense heat — H100/H200/B200 servers run at 5–10 kW per server, far beyond what standard air conditioning can handle. The MCPU is a dedicated outdoor liquid cooling unit that absorbs this heat via a coolant loop, transfers it to an outdoor dry cooler or fluid circuit, and rejects it to the atmosphere. It also provides clean, conditioned power distribution and can include UPS bypass and generator interfaces for enterprise deployments.

MCPU-S — Small · up to 15 kW

Specification
Cooling capacityUp to 15 kW
Cooling typeDry cooler · wall-mount
Footprint600×400mm wall bracket
Power required32A 3-phase dedicated circuit
RefrigerantGlycol/water loop · no refrigerant
Noise~58 dBA at 1m
IP ratingIP55 · C4 cyclone rated
Suited to
Starter Plus (8× RTX 4090, 4.1 kW) · Pro Plus (8× RTX PRO 6000, 4.8 kW) · Enterprise H100 NVL (4.6 kW) · Campus M configurations
Installed cost estimate
$8,000–$14,000
Includes unit, coolant pipes, pump station, commissioning

MCPU-M — Medium · up to 60 kW

Specification
Cooling capacityUp to 60 kW
Cooling typeDry cooler array · 2–4 panels
Footprint2.4×1.2m ground pad
Power required63A 3-phase dedicated circuit
RefrigerantGlycol/water loop
IP ratingIP55 · C4 cyclone rated
Suited to
Enterprise H100 SXM5 (7.7 kW) · Enterprise H200 (8.4 kW) · Enterprise B200 (9.7 kW) · Multi-server UNIT-A1/A2 configurations
Installed cost estimate
$35,000–$65,000
Includes unit, coolant loop, CDU, manifold, commissioning

MCPU-L — Large · up to 140 kW

Specification
Cooling capacityUp to 140 kW
Cooling typeFull outdoor fluid pod
Footprint3.0×2.0m ground pad + pump skid
Power required125A 3-phase dedicated circuit
IP ratingIP55 · C4 cyclone rated
Suited to
Sovereign NVL72 (120 kW) · Multi-pod UNIT-A2 clusters · Full campus deployments
Installed cost estimate
$120,000–$200,000
Includes fluid pod, pump skid, coolant loop, UNIT-A2 manifold, commissioning

Enclosure Guide — Australian Conditions

Physical housing for every GridMind module tier. Three enclosure types covering SMB outdoor units, single-storey UNIT-A1 Kingspan modular pods, and dual-storey UNIT-A2 enterprise pods. All rated to cyclone C4, designed for Queensland tropical conditions.

SMB Outdoor Enclosure — IP55-rated anodised aluminium, CNC-engraved. Installs beside any building like a split-system AC unit. Half a day, no DA required, no MCPU. Covers Spark (desktop), Starter, and Starter Plus.
Form factor
Outdoor unit
IP rating
IP55
Cyclone rated
C4
Install time
4–6 hours
DA required
No
Rack configuration diagrams — Starter and Starter Plus
GridMind Starter — 4× RTX 4090
GRIDMIND STARTER 4× RTX 4090 · Outdoor IP55 unit AMD Threadripper PRO CPU 280W TDP 128 GB DDR5 6× DIMM 2× NVMe 4 TB Samsung 990 Pro RTX 4090 #1 24 GB GDDR6X · 450W RTX 4090 #2 24 GB GDDR6X · 450W RTX 4090 #3 24 GB GDDR6X · 450W RTX 4090 #4 24 GB GDDR6X · 450W 2× PSU · GSOL SBC 80+ Titanium · management 96 GB VRAM · 2.1 kW · air cooled · IP55
GridMind Starter Plus — 8× RTX 4090
GRIDMIND STARTER PLUS 8× RTX 4090 · Outdoor IP55 · same enclosure as Starter AMD Threadripper PRO CPU · 300W 256 GB DDR5 · 2× NVMe 4TB 256 GB DDR5 ECC · 2× NVMe 2× PSU 1,600W · 10GbE · GSOL SBC RTX 4090 #1 24 GB GDDR6X · 450W RTX 4090 #2 24 GB GDDR6X · 450W RTX 4090 #3 24 GB GDDR6X · 450W RTX 4090 #4 24 GB GDDR6X · 450W RTX 4090 #5 24 GB GDDR6X · 450W RTX 4090 #6 24 GB GDDR6X · 450W RTX 4090 #7 24 GB GDDR6X · 450W RTX 4090 #8 24 GB GDDR6X · 450W 192 GB VRAM · 4.1 kW · air cooled · IP55
Physical specs (outdoor enclosure): 680mm × 860mm × 600mm (Starter) / 950mm × 860mm × 600mm (Starter Plus) · IP55 dust + water jet · C4 cyclone rated · 20A single-phase (Starter) / 32A 3-phase (Starter Plus) · no DA required — same regulatory category as outdoor AC unit · installs in 4–6 hours.
Pro Outdoor Enclosure — Same IP55 outdoor enclosure as Starter, housing RTX PRO 6000 Server Ed. cards. ECC memory, NVIDIA AI Enterprise certified, passive server-grade cooling. Upgrade from Pro to Pro Plus by adding 4 cards — same enclosure, no new pad.
Form factor
Outdoor unit
IP rating
IP55
ECC memory
Yes
AI Enterprise
Certified
Upgrade path
Pro → Pro Plus
Rack configuration diagrams
GridMind Pro — 4× RTX PRO 6000 Server Ed.
GRIDMIND PRO 4× RTX PRO 6000 Server Ed. · outdoor SMB unit AMD Threadripper PRO 7995WX 96 cores · 300W TDP · sTR5 256 GB DDR5 ECC RDIMM 2× NVMe 4TB · 10GbE NIC RTX PRO 6000 Server Ed. #1 96 GB GDDR7 ECC · 450W passive · dual-slot RTX PRO 6000 Server Ed. #2 96 GB GDDR7 ECC · 450W passive · dual-slot RTX PRO 6000 Server Ed. #3 96 GB GDDR7 ECC · 450W passive · dual-slot RTX PRO 6000 Server Ed. #4 96 GB GDDR7 ECC · 450W passive · dual-slot 2× PSU 1,200W · GSOL SBC · 10GbE 192 GB ECC GDDR7 · 2.4 kW · IP55 · air cooled
GridMind Pro Plus — 8× RTX PRO 6000 Server Ed.
GRIDMIND PRO PLUS 8× RTX PRO 6000 Server Ed. · same outdoor enclosure as Pro AMD Threadripper PRO 7995WX 96 cores · 300W TDP · sTR5 · Zen 4 256 GB DDR5 ECC · NVMe · 10GbE 2× NVMe 4TB RAID · GSOL SBC RTX PRO 6000 Server Ed. #1 96 GB GDDR7 ECC · 450W passive · dual-slot RTX PRO 6000 Server Ed. #2 96 GB GDDR7 ECC · 450W passive · dual-slot RTX PRO 6000 Server Ed. #3 96 GB GDDR7 ECC · 450W passive · dual-slot RTX PRO 6000 Server Ed. #4 96 GB GDDR7 ECC · 450W passive · dual-slot RTX PRO 6000 Server Ed. #5 96 GB GDDR7 ECC · 450W passive · dual-slot RTX PRO 6000 Server Ed. #6 96 GB GDDR7 ECC · 450W passive · dual-slot RTX PRO 6000 Server Ed. #7 96 GB GDDR7 ECC · 450W passive · dual-slot RTX PRO 6000 Server Ed. #8 96 GB GDDR7 ECC · 450W passive · dual-slot 2× PSU 1,600W · GSOL SBC · 10GbE 384 GB ECC GDDR7 · 4.8 kW · IP55 · air cooled
RTX PRO 6000 Server Ed. vs RTX 4090: 96 GB ECC GDDR7 (vs 24 GB GDDR6X no ECC) · passive server-grade cooling · dual-slot 267mm · NVIDIA AI Enterprise certified · ISV certifications (Ansys, Autodesk, VMware) · 24/7 validated. Suits APRA CPS 234, IRAP SENSITIVE, and clinical AI workloads.
UNIT-A1 — Single-storey modular pod — Kingspan KS1000 PIR 100mm insulated panel construction. 6×8m external footprint, 48m² internal floor area, 3,600mm clear height. NCC Class 8. DA required 6–14 weeks. Hot/cold aisle separation. Raised access floor 400mm void.
Footprint
6×8m (48m²)
Clear height
3.6m
Wall insulation
Kingspan 100mm PIR
Floor loading
12 kN/m²
Cyclone
C4 · AS/NZS 1170.2
DA
6–14 weeks
Rack configuration diagrams — Enterprise tiers
Enterprise — 8× H100 NVL
ENTERPRISE — 8× H100 NVL UNIT-A1 Kingspan pod · MCPU-S dry cooler InfiniBand HDR switch · 200G fabric · 40-port 10GbE management switch 2× Xeon Platinum 8480+ 512 GB DDR5 ECC · dual socket · NVMe RAID H100 NVL #1 94 GB HBM2e · 400W · PCIe Gen5 H100 NVL #2 94 GB HBM2e · 400W · PCIe Gen5 H100 NVL #3 94 GB HBM2e · 400W · PCIe Gen5 H100 NVL #4 94 GB HBM2e · 400W · PCIe Gen5 H100 NVL #5 94 GB HBM2e · 400W · PCIe Gen5 H100 NVL #6 94 GB HBM2e · 400W · PCIe Gen5 H100 NVL #7 94 GB HBM2e · 400W · PCIe Gen5 H100 NVL #8 94 GB HBM2e · 400W · PCIe Gen5 3-phase PDU · 2× PSU 2,000W · GSOL SBC 640 GB HBM2e · 4.6 kW · MCPU-S liquid cooling
Enterprise — 8× H100 SXM5
ENTERPRISE — 8× H100 SXM5 UNIT-A1 Kingspan pod · MCPU-M liquid cooling InfiniBand HDR200 switch · 200G fabric 10GbE management switch 2× Xeon Platinum 8480+ 1 TB DDR5 ECC · SXM5 baseboard host · NVMe H100 SXM5 #1 80 GB HBM2e · 700W · SXM5 baseboard H100 SXM5 #2 80 GB HBM2e · 700W · SXM5 baseboard H100 SXM5 #3 80 GB HBM2e · 700W · SXM5 baseboard H100 SXM5 #4 80 GB HBM2e · 700W · SXM5 baseboard H100 SXM5 #5 80 GB HBM2e · 700W · SXM5 baseboard H100 SXM5 #6 80 GB HBM2e · 700W · SXM5 baseboard H100 SXM5 #7 80 GB HBM2e · 700W · SXM5 baseboard H100 SXM5 #8 80 GB HBM2e · 700W · SXM5 baseboard 3-phase PDU · UPS bypass · GSOL SBC 640 GB HBM2e · 7.7 kW · MCPU-M cooling
Hardware configurations
Enterprise H100 NVL8× H100 NVL · 640 GB HBM2e · 4.6 kW · MCPU-S
Enterprise H100 SXM58× H100 SXM5 · 640 GB HBM2e · 7.7 kW · MCPU-M
Max racks in A16× standard 19″ 800mm deep
Hot/cold aisleCold 1,200mm · hot 900mm contained
Raised floor void400–600mm for cold air distribution
H100 NVL vs H100 SXM5: NVL is PCIe Gen5 form factor — fits standard server. SXM5 requires a dedicated SXM5 host baseboard and commands higher per-GPU throughput for LLM training workloads.
UNIT-A2 — Dual-storey enterprise pod — Upper floor: compute rack room with raised access floor. Lower floor: power distribution, UPS, switchboard, GSOL control node. Rooftop solar + HVAC array. MCPU-M coolant manifold on rear wall. Side-mounted access stair. ~96–120m² total.
Total area
~96–120m²
Upper height
3.4m clear
Lower height
3.2m clear
Wall insulation
Kingspan 150mm PIR
Floor loading
20 kN/m² upper
Cyclone
C4 · engineer certified
Rack configuration diagrams — Enterprise Plus tiers
Enterprise Plus H200 — 8× H200 SXM5
ENTERPRISE PLUS H200 — 8× H200 SXM5 UNIT-A2 dual-storey pod · MCPU-M · DLC required InfiniBand NDR400 switch · 400G fabric 100GbE management · out-of-band BMC 2× Xeon Platinum 8592+ 2 TB DDR5 ECC · SXM5 host baseboard · NVMe RAID H200 SXM5 #1 141 GB HBM3e · 1,000W · SXM5 H200 SXM5 #2 141 GB HBM3e · 1,000W · SXM5 H200 SXM5 #3 141 GB HBM3e · 1,000W · SXM5 H200 SXM5 #4 141 GB HBM3e · 1,000W · SXM5 H200 SXM5 #5 141 GB HBM3e · 1,000W · SXM5 H200 SXM5 #6 141 GB HBM3e · 1,000W · SXM5 H200 SXM5 #7 141 GB HBM3e · 1,000W · SXM5 H200 SXM5 #8 141 GB HBM3e · 1,000W · SXM5 3-phase PDU 125A · UPS · GSOL SBC 1.1 TB HBM3e · 8.4 kW · MCPU-M liquid cooling
Enterprise Plus B200 — 8× B200 Blackwell
ENTERPRISE PLUS B200 — 8× B200 Blackwell UNIT-A2 dual-storey pod · MCPU-M · DLC-2 required InfiniBand NDR800 switch · 800G fabric 100GbE management · out-of-band BMC 2× Xeon Platinum 8592+ 2 TB DDR5 ECC · GB200 baseboard · NVMe RAID B200 Blackwell #1 192 GB HBM3e · 1,000W · 10U chassis B200 Blackwell #2 192 GB HBM3e · 1,000W · 10U chassis B200 Blackwell #3 192 GB HBM3e · 1,000W · 10U chassis B200 Blackwell #4 192 GB HBM3e · 1,000W · 10U chassis B200 Blackwell #5 192 GB HBM3e · 1,000W · 10U chassis B200 Blackwell #6 192 GB HBM3e · 1,000W · 10U chassis B200 Blackwell #7 192 GB HBM3e · 1,000W · 10U chassis B200 Blackwell #8 192 GB HBM3e · 1,000W · 10U chassis 3-phase PDU 125A · UPS · GSOL SBC 1.5 TB HBM3e · 9.7 kW · MCPU-M liquid cooling
UNIT-A2 upper floor — Compute rack room
Raised access floor 400mm void for cold air distribution · under-floor cable trays · hot-aisle containment · 6–8 racks · InfiniBand NDR400/800 fabric · MCPU-M coolant loop manifold connection on rear wall.
UNIT-A2 lower floor — Power and operations
250–400A 3-phase main switchboard · N+1 UPS rated to full rack load · generator input terminals · GSOL control node · operations console · battery room · cable tray (power separated from data 300mm).

Compliance Framework

Every GridMind commercial deployment operates within Australian compliance frameworks. Understanding the compliance tier your customer needs determines both the hardware selection and the rate premium they can command through GSOL.

Standard Commercial

GSOL rate tierOpen market
Hardware requiredAny tier
Data residencyAU-based recommended
Who uses thisSMBs, startups, universities
Rate premiumNone — market rate

IRAP SENSITIVE

GSOL rate tierReserved premium
Hardware requiredRTX PRO 6000 min.
Data residencyAU sovereign required
Who uses thisAPS agencies, banks, health
Rate premium~40–60% over open market

IRAP PROTECTED

GSOL rate tierSovereign
Hardware requiredH100+ · Campus M min.
Data residencyAir-gapped or sovereign pod
Who uses thisDefence, ASD, ASIS, classified
Rate premium~80–150% over open market
APRA CPS 234: Financial institutions (banks, insurers, super funds) must comply with APRA CPS 234 for information security. GridMind nodes with RTX PRO 6000 Server Ed. (NVIDIA AI Enterprise certified) satisfy the hardware requirements for financial workloads. GSOL provides audit logs, access controls, and data residency certification letters on request.

AI Demand Stack — What the Forecasts Miss

All current AU demand forecasts (AEMO, M3 Property, Oxford Economics) are based on Wave 1 workloads only. The unmodelled next wave — physical robotics, autonomous vehicles, personal AI agents, defence, and biotech — will dwarf current projections from 2028 onwards.

Australian AI compute demand stack 2022–2035, showing current forecasted workloads and the unmodelled next-wave demand from robotics, autonomous vehicles, personal AI agents, defence, and biotech — illustrating how all current forecasts systematically undercount future demand.

Australian AI compute demand 2022–2035 broken into eight demand layers — enterprise LLM, cloud AI, government AI, health AI, robotics, autonomous vehicles, personal AI agents, defence AI, and biotech autonomous labs — showing that current industry forecasts capture only the bottom four layers while the upper four remain entirely unmodelled.
What current forecasts capture vs what they miss
Modelled by AEMO / M3 / Oxford
Enterprise LLM inference · Cloud AI APIs · Government digital services · Hospital / clinical AI · University research compute · Financial AI (fraud, credit)
Not in any current forecast
Physical robotics inference (humanoid, industrial) · Autonomous vehicles (V2X, real-time perception) · Personal AI agents (always-on, device + cloud hybrid) · Defence / sovereign AI (classified workloads) · Autonomous biotech labs · AGI-class frontier training
Sources: AEMO 2025 ISP, M3 Property Nov 2025, Oxford Economics Jul 2025, Deloitte Insights Nov 2025, McKinsey Technology Report 2025, RAND AI Power Requirements 2025, Bain Technology Report 2025. Next-wave layers are author projections based on known deployment trajectories — not yet in any published AU forecast model.

Power & Infrastructure — Founder Reference

Everything a founder needs to know about power, connectivity, and physical infrastructure requirements for GridMind deployments. Use this panel to answer customer site assessment questions on the spot.

ModuleTotal drawCircuit requiredPhaseMeter upgrade?
Spark0.45 kW10A single-phaseSingleNo — standard outlet
Starter2.1 kW20A single-phase dedicatedSingleUsually no
Starter Plus4.1 kW32A 3-phase3-phaseUsually no
Pro2.4 kW20A single-phase dedicatedSingleUsually no
Pro Plus4.8 kW32A 3-phase3-phaseCheck with Energex
Enterprise (H100 NVL)4.6 kW63A 3-phase3-phaseLikely — contact Energex
Enterprise Plus H2008.4 kW125A 3-phase3-phaseYes — dedicated supply
Enterprise Plus B2009.7 kW125A 3-phase3-phaseYes — dedicated supply
QLD electricity tariff: Energex standard business tariff is $0.35/kWh (2026). Large deployments (>10 kW) may qualify for ToU (time-of-use) tariffs which can reduce costs during off-peak GSOL hours. GridMind recommends customers speak to their energy retailer about ToU pricing before installation.
Minimum requirements
Connection typeNBN or fibre preferred
Upload speed100 Mbps minimum
Download speed100 Mbps minimum
Latency<50ms to AU IX
Static IPRequired for GSOL
Port forwardingGSOL agent handles automatically
Recommended
Enterprise nodes1 Gbps fibre dedicated
GSOL backup4G/5G SIM failover included
ManagementOut-of-band via GSOL SBC
On-site switch10GbE for rack nodes

SMB site checklist

☐ Flat concrete area ≥1.2×0.8m for unit
☐ 600mm clearance in front of fan face
☐ Existing 20A circuit nearby (or licensed electrician to install)
☐ Cat6A cable run to nearest switch (<100m)
☐ NBN or fibre connection with static IP
☐ No DA required (same as outdoor AC unit)

Enterprise site checklist

☐ DA lodged (6–14 week lead time)
☐ RC slab 200mm poured and cured
☐ 3-phase power connection confirmed with Energex
☐ MCPU-S/M/L pad and clearance allocated
☐ 1 Gbps fibre to site confirmed
☐ RPEQ engineer engaged for cyclone certification
☐ QBCC licensed builder engaged
Infrastructure itemWho arrangesTypical costNotes
Concrete pad (SMB)Customer$300–$800100mm, 1.2×0.8m. Standard concretor.
Concrete slab (UNIT-A1)GridMind / customer$8,000–$18,000200mm RC slab, N12 mesh, engineer-designed.
Electrical circuit (SMB)Customer$400–$1,200Licensed electrician. 20A dedicated run.
3-phase power connectionCustomer + Energex$2,000–$12,000Energex connection fee + internal wiring.
NBN static IP upgradeCustomer$20–$50/mo extraMost ISPs offer static IP as add-on.
GSOL commissioningGridMindIncludedRemote — typically 2 hours after power-on.
DA (Development Application)Customer + GridMind support$3,000–$8,000Council fees + private certifier. Enterprise only.

GridMind Module Designer

AI-assisted architecture for both unit types. Configure your requirements, generate a full design brief, materials schedule, compliance checklist, production-line assembly sequence, and dimensional drawings — ready to hand to a builder, engineer, or manufacturer.

Step 1 of 3 — configure your module requirements
Tell the AI designer what you need. It will generate a complete design brief.
Select your unit type, hardware tier, site location, and special requirements. The AI will generate a full specification including structure, insulation, cooling integration, electrical, floor loading, and NCC compliance notes.
Unit type and hardware
Module type
Primary hardware tier
Number of racks / servers in this module 2
2
MCPU cooling tier required
Site and environment
Site location (wind/cyclone region)
Ambient temperature range
Site classification (NCC)
Special requirements
Live configuration preview
Unit type
UNIT-A1
Internal floor area
48 m²
Total IT load
15.4 kW
Structural type
SHS steel frame
Kingspan KS1000 75mm PIR
Wall R-value
R4.0
Floor loading spec
12 kN/m²
Cyclone rating
C4
NCC class
10a
No DA typically required
Configure your requirements above to see the design brief. The AI will generate a full specification including materials schedule, compliance notes, and production sequence.
Generating design brief...
Materials specification — Australian suppliers · NCC compliant
Standard materials schedule for GridMind modular units
Every material is specified for Australian conditions: cyclone regions C2–C4, tropical/subtropical humidity, UV exposure, coastal salt air, and seismic zones. All products are available from Australian suppliers with standard lead times.
Primary structure — SHS (Square Hollow Section) steel frame
ComponentSpecificationStandardSupplier (AU)Notes
Corner postsSHS 150×150×6 G350 hot-dip galvanisedAS/NZS 1163OneSteel / InfraBuildPrimary load-bearing. Bolt-down to slab with M20 anchor bolts.
Floor beamsSHS 100×100×5 G350 galv.AS/NZS 1163OneSteel / InfraBuildSpan 2.5–3.0m centres. Design for 12 kN/m² floor load (server racks).
Roof purlinsLipped C-section 150×65×2.4mm G450AS/NZS 4600Lysaght / StramitSpan tables AS/NZS 4600. Max 1.2m centres in cyclone C4.
BracingFlat strap 75×6mm G350 + turnbucklesAS 4100OneSteel / InfraBuildDiagonal wall bracing for racking resistance. Engineer-designed in C3/C4.
ConnectionsGrade 8.8 M16/M20 bolts + structural cleatsAS 4100Hobson / TFCAll connections engineer-certified. No site welding required — bolt-together for production line.
Base frameRHS 200×100×8 G350 perimeter + levelling feetAS/NZS 1163OneSteelFactory-welded base frame. Hot-dip galv after fabrication. Anchor to 200mm slab.
Wall panels — Kingspan KS1000 AWP insulated panel system
Why Kingspan KS1000 AWP: PIR foam core (polyisocyanurate — thermosetting, forms fire-resistant char, does not melt or drip). Highest R-value per mm of any commercial insulation panel. Australian-manufactured (Kingspan AU, CodeMark certified). Factory tongue-and-groove joint — zero site-cut thermal bridging. Available in Colorbond colour range. 30-year warranty. Used in pharmaceutical cold stores, food processing, and data centres globally.
ApplicationPanel typeThicknessR-valueWind ratingFire (FRR)Notes
External walls — Tier 1 (SMB, Starter Plus)Kingspan KS1000 AWP PIR75mmR4.0C3 standard · C4 with eng. fix–/–/– (non-rated) or 30/30/30 with liningAdequate for ≤10 kW nodes. Class 10a sufficient.
External walls — Tier 2 (H100, H200)Kingspan KS1000 AWP PIR100mmR5.3C4 with eng. fix60/60/60 with GypRoc Fyrchek liningRequired for Class 8. Provides thermal mass for 40–50 kW load.
External walls — Tier 3 (B200, Sovereign)Kingspan KS1000 AWP PIR150mmR8.0C4 engineer-certified90/90/90 with 2× GypRoc liningMaximum insulation for high-heat nodes. Class 8 mandatory.
Roof panel — all tiersKingspan KS1000 RW PIR100mmR5.3 (roof)AS/NZS 1170.2 C460/60/60Low-slope (≥3°) or flat with falls. Concealed fix Kliplok profile. Solar PV-mount-compatible.
Internal partition — hot/cold aisleKingspan KS1000 inner50mmR2.5Internal only30/30/30Hot-aisle containment wall. Non-structural — bolts to server rails or ceiling track.
Panel dimensions: 1,000mm wide × up to 12,000mm length (factory-cut to requirement). Steel skins: 0.5mm Colorbond Ultra (coastal XRW grade for salt air). Panel weight: ~12–18 kg/m² depending on thickness. Colour: Woodland Grey external / White internal (standard).
Floor system
TierFloor specLoad ratingNotes
SMB (Starter / Spark / Pro)IP55 outdoor enclosure — no floor slab requiredN/AUnit mounts to concrete pad via M12 anchor bolts. No raised floor.
UNIT-A1 (Starter Plus / Enterprise)200mm reinforced concrete slab, N12 mesh, 32 MPa concrete12 kN/m² concentratedEngineer-designed. Anti-vibration isolation pads under rack feet. 75mm screed over slab optional.
UNIT-A1 with raised floor optionTate Systems / Kingspan Access Floor 600×600mm panels12 kN/m² (14 kN/m² heavy)400–600mm void for cold-air distribution and cable management. Perforated tiles at cold aisle (25% open area). Static-dissipative surface.
UNIT-A2 Ground floor (power/ops)200mm RC slab N16 mesh, 40 MPa concrete15 kN/m²UPS, switchboard, and PDU loads. UPS units to 2,000 kg each — dedicated pad isolation required.
UNIT-A2 Upper floor (compute)300mm RC slab post-tensioned or 250mm RC + Bondek deck20 kN/m² UDLSovereign-tier rack = 1,350 kg point load. Structural engineer certification mandatory.
Electrical and power distribution
ComponentSpecificationStandardNotes
Main switchboard415V 3-phase, rating per tier (see below)AS/NZS 3000 Wiring RulesFactory-built, tested, certified prior to delivery. IP54 rating minimum.
SMB Starter switchboard32A single-phase MCB + RCD + surge protectionAS/NZS 3000Dedicated 20A circuit for compute. 10A for auxiliary. Standard 8-way switchboard.
UNIT-A1 switchboard125A 3-phase, 12-way, with 63A sub-board for computeAS/NZS 3000Compute sub-board: per-rack 32A breakers. MCPU-S: 32A 3-phase dedicated circuit.
UNIT-A2 switchboard250A–400A 3-phase main, distribution boards per floorAS/NZS 3000Separate distribution for compute, cooling, lighting, UPS. MCPU-M: 63A 3-phase dedicated.
UPS systemEaton 9PX or APC Smart-UPS SRT, N+1 configurationAS/NZS 450910-minute bridge minimum. Tier 1: 3–5 kVA. UNIT-A1: 20–40 kVA. UNIT-A2: 100–200 kVA.
Cable trayUnistrut perforated steel tray, 300–600mm wideAS/NZS 3000Overhead route — above racks in compute room. Separate power and data trays (min 300mm separation).
Earthing25mm² green/yellow TPS to earth stake, mesh bondingAS/NZS 3000 Pt 5All metalwork bonded. Static dissipative floor bonded to earth mesh. Lightning protection per AS/NZS 1768 in C3/C4 regions.
Rooftop — solar and HVAC integration
ItemSpecificationNotes
Solar PV panelsJinko / LONGi 415W monofacial panels, 25-year warrantyRoof-mount on factory-installed Colorbond standing seam rails. 6–12 panels standard (2.5–5 kW offset). Isolator per AS/NZS 5033.
Solar inverterSolarEdge / Fronius 3-phase 5–10 kWGrid-tied, export-limited. AS/NZS 4777 compliant. Mounted internally in electrical room.
Rooftop HVAC unitDaikin / Mitsubishi commercial packaged unit, 10–25 kWBuilding envelope heating only — not compute cooling. Maintains ambient ≤28°C in electrical room and operations area.
MCPU dry cooler (rooftop option)Vertiv CoolChip or nVent CX121 roof-mount variantUNIT-A1 only. Connects to CDU manifold via insulated copper pipes through roof penetration. Engineer-designed penetration with flashing.
Australian building compliance — NCC 2025 · QBCC · AS/NZS standards
Compliance requirements by unit type and NCC class
Building classification determines what approvals you need before you can build. The difference between Class 10a and Class 8 is significant — it determines whether you need a Development Application (DA), a structural engineer, fire engineering, and access compliance. Choose the right class at the start.
NCC building class decision guide
Class 10a Fastest path
Non-habitable structure. Sheds, garages, utility buildings. No one works inside for extended periods. No sanitary facilities required.
Development Application (DA)Often exempt (state rules vary)
Building certifierRequired but low-bar
Structural engineerRequired for C3/C4 cyclone
Fire engineeringNot required typically
Access (DDA)Not required
Electrical certLicensed electrician cert required
GridMind unitsStarter · Spark · Pro · Starter Plus
Typical approval time2–6 weeks
Class 8 Standard commercial
Laboratories, workshops, and buildings where hazardous processes occur. Commercial data centre infrastructure where people regularly work inside classifies here.
Development Application (DA)Required — Council lodgement
Building certifierPrivate or council certifier
Structural engineerMandatory — certified drawings
Fire engineeringPerformance solution likely needed
Access (DDA)Required — path of travel to entry
Electrical certQBCC licensed + test & tag
GridMind unitsUNIT-A1 · UNIT-A2 · all enterprise
Typical approval time6–14 weeks
Class 5 / 6 If customer-facing
Office buildings or retail. Only applies if GridMind operates a shared-access facility where members of the public or third-party tenants access the space directly and regularly.
Development ApplicationMandatory — full DA + EIS
Fire engineeringFull FER + sprinklers likely
Access (DDA)Full DDA + accessible toilet
Energy efficiencyNCC Section J compliance
When this appliesCo-lo / shared access facility only
Typical approval time4–12 months
Queensland-specific requirements (QBCC)
RequirementAuthorityWhen requiredNotes
Building permitQBCC / Private certifierAll Class 8 and most Class 10a >10m²File Form 5 (building permit application) with local council or private certifier. Include structural drawings.
Electrical safety certificateElectrical Safety Office (ESO)All new electrical work >50V ACLicensed electrician issues ESO Form 1. Required before energisation. RPEQ for installations >100A.
Plumbing permitQBCC PlumbingOnly if sanitary facilities installedNot required for compute-only UNIT-A1. Required if UNIT-A2 includes amenities or floor drain.
Cyclone tie-down certificationStructural engineer (RPEQ)All C2–C4 wind regionsEngineer must certify connection of panel to frame, frame to slab, slab to ground. Mandatory in North QLD.
QBCC builder licenceQBCCAll construction >$3,300 valueBuilder must hold QBCC licence. Subcontractors (electrician, plumber) must hold own licences.
AS/NZS 3000 electrical inspectionLicensed electrical inspectorAll Class 8 switchboard installationsThird-party inspection of switchboard and distribution. Required for insurance.
Key Australian standards referenced
StandardCovers
AS/NZS 1163Cold-formed steel hollow sections (SHS, RHS)
AS 4100Steel structures — design and construction
AS/NZS 4600Cold-formed steel structures (purlins, girts)
AS/NZS 1170.2Wind actions — cyclone loading design
AS 1170.1Structural loads — dead, live, snow loads
AS 3600Concrete structures — slab design
StandardCovers
AS/NZS 3000Electrical wiring rules (the "Wiring Rules")
AS/NZS 4509Stand-alone power systems (UPS)
AS/NZS 5033Solar PV system installation
AS/NZS 1768Lightning protection
AS/NZS 1530Fire resistance testing for building materials
AS 1851Fire protection system maintenance
Reference drawings — not for construction use without engineering sign-off
Dimensional drawings — UNIT-A1 and UNIT-A2
These are schematic reference drawings showing key dimensions, structural grid, panel layout, and service zone allocations. They are not stamped construction drawings. For construction, a licensed RPEQ structural engineer must certify site-specific versions of these drawings.
Select drawing to view
UNIT-A1 — Single-storey modular compute pod — Floor plan (schematic) Not for construction · RPEQ certification required
Key dimensions reference
Factory production — off-site manufacturing for consistent quality
GridMind module production line — from factory to site in 5 steps
Designed for mass production. Every module is built to the same engineering drawings with the same certified materials. Off-site manufacturing means weather delays don't stop production, quality is consistent, and the site installation time is minimal — 1 to 5 days depending on unit type.
Production sequence — UNIT-A1 (single storey)
1
Factory — base frame fabrication (Day 1–2)
RHS 200×100×8 perimeter frame factory-welded and jig-drilled on flat steel table. All connection holes CNC-punched to drawings. Hot-dip galvanise after fabrication. Weld inspected to AS/NZS 1554. Corner post sockets welded and certified. Levelling feet or forklift pockets welded. This is the only welding in the entire build — everything else is bolted.
2
Factory — wall panel and roof prefabrication (Day 2–4)
Kingspan KS1000 AWP panels cut to length on factory saw — zero site cutting. All openings (door frames, MCPU penetrations, cable entry points, louvre openings) pre-cut and frame-fitted in factory. Panels labelled and stacked per installation sequence drawing. Roof panels pre-drilled for solar mounting rails. HVAC curb pre-welded to roof panel at factory.
3
Factory — electrical and mechanical pre-build (Day 3–5)
Switchboard built, wired, tested, and certified off-site. Cable tray sections pre-cut. PDU units pre-wired and tested. UPS unit checked and firmware-loaded. Rooftop HVAC unit assembled and pressure-tested. MCPU manifold (if applicable) pre-piped with pressure test cert. All components packed in numbered crates matching installation sequence.
4
Site — concrete slab and anchor installation (Day 1–3 on site, concurrent with factory steps 1–3)
200mm reinforced concrete slab poured by local concrete contractor. Cast-in M20 anchor bolts per engineer's layout drawing. Base frame delivered and set on slab — level checked. Base frame bolted down. All anchor bolt positions match factory-jig-drilled base frame holes exactly — no site drilling. Slab cure 7 days before structural frame loaded.
5
Site — frame erection and panel installation (Day 4–6 on site)
Corner posts bolted to base frame — 2 person crew + telehandler. Roof purlins bolted. Wall panels installed starting from corner — tongue-and-groove joints engaged, factory screws at 300mm centres. Door frames (pre-hung, factory-fitted) installed. Roof panels installed. All penetrations sealed with factory-supplied PIR foam backer rod and Sika Flex 11FC sealant. Cyclone bracing straps installed and torqued to specification.
6
Site — electrical and mechanical connection (Day 6–8)
Switchboard lifted into electrical room — pre-wired, bolt-to-wall. Cable tray installed overhead. Server racks positioned and bolted to floor anti-seismic rails. Power feeds connected from switchboard to rack PDUs. MCPU outdoor module positioned on pad and coolant loop connected (if applicable). Solar PV panels mounted on pre-installed roof rails. All systems energised and tested. ESO electrical certificate issued.
7
Site — commissioning and GSOL registration (Day 8–10)
GSOL agent software installed on management node. Hardware discovered and registered. First inference job dispatched as commissioning test. MIST Ergon type test certificate (from Phase 1 testing) referenced for compliance record. Building certifier final inspection. QBCC compliance certificate issued. GridMind dashboard live — node registered, earnings commence within 30 days.
Production timeline summary
ActivityStarter / SMBUNIT-A1UNIT-A2Where
Base frame fabrication2 days3 daysFactory
Panel prefabrication2 days3 daysFactory
Electrical pre-build1 day3 days5 daysFactory
Concrete slab (concurrent)– (concrete pad 1 day)3 days4 daysSite
Structure erection4 hrs2 days3 daysSite
Electrical + mechanical4 hrs2 days3 daysSite
Commissioning + GSOL2 hrs1–2 days2–3 daysSite
Total calendar time1–2 days8–10 days12–16 days
Crew required (site)2 people4–6 people6–8 people + crane
Specialist tradesElectrician (1 day)Electrician + concreterElectrician + concreter + structural steel erector
Key manufacturing principle — zero site welding: Every structural connection in the GridMind module is bolted, not welded. This is critical for production-line manufacturing: bolt connections require no certified welders on site, no weld inspections, no NDT (non-destructive testing) in the field. The only welding is the factory base frame fabrication and any custom brackets — all done in a controlled environment with certified welders and jigs, inspected before shipment. This reduces site risk, speeds erection, and enables any licensed builder to assemble the module without specialist structural steel expertise.

Hardware Expertise — Computer & AI Engineer Reference

Everything a computer engineer and AI software engineer would know about matching hardware to workloads — written for founders without that background. Use this to understand what hardware is needed, why, and how to explain it confidently to any technical customer or procurement team.

Computer engineer perspective — the most important question
What type of AI work is the customer actually doing?
Every hardware decision flows from this one question. The same GPU cluster that runs inference on a live chatbot is the wrong choice for training a new model. Get this wrong and either the hardware is too small to do the job, or the customer is massively overpaying for capacity they'll never use.
The four fundamental AI workload types
1. Inference — "serving" a trained model
The model is already trained. A user sends a question, the GPU processes it and returns an answer. This is what 95% of GridMind customers need. Low latency matters. High concurrency matters. Exact precision doesn't — you can use FP8 or INT4 quantisation to fit larger models in less VRAM.
Customer examples: Hospital chatbot answering doctor queries · Bank fraud alert system · Government document summariser · School AI tutor
Optimal hardware
RTX 4090 / H100 NVL / H200
VRAM per request
Low — model fits in VRAM once
Precision
FP16 / FP8 / INT4 — all fine
GridMind fit
Excellent — all node tiers
2. Training — teaching a model from scratch
The model does not yet exist. You feed it millions of examples over days or weeks and the GPU computes the weight adjustments. Needs massive VRAM. Needs high-bandwidth interconnect between GPUs (NVLink) so all GPUs share the model. Very few GridMind customers will do this — only large universities and research organisations.
Customer examples: University AI lab training a climate model · Research institute training on Indigenous language data · Large hospital training a radiology model on Australian patient data
Optimal hardware
H100 SXM5 / H200 SXM5 / B200
VRAM required
Very high — 640 GB – 1.5 TB
Interconnect
NVLink mandatory (not PCIe)
GridMind fit
Enterprise+ only
3. Fine-tuning — adapting an existing model
Take a large pre-trained model (e.g. Llama 3.1) and continue training it on your specific data so it learns your domain. Requires less compute than full training but still needs significant VRAM. Most common use case: a bank wants a model that understands Australian financial regulations, or a hospital wants a model that knows their specific clinical protocols.
Customer examples: Government agency fine-tuning on policy documents · Legal firm adapting Llama for Australian law · Hospital adapting a base model for their drug formulary
Optimal hardware
H100 NVL / H200 / RTX PRO 6000
VRAM required
Medium — 96 GB – 640 GB
Time
Hours to days (not weeks)
GridMind fit
Good — Pro, Enterprise, H100
4. Embedding / RAG — search and retrieval AI
Convert documents into numeric vectors (embeddings) so an AI can search through thousands of documents and find relevant context before answering. Very low VRAM per document. Can run on smaller GPUs. Often combined with inference in a RAG (Retrieval-Augmented Generation) pipeline — search for relevant docs, then feed them to the LLM to generate an answer.
Customer examples: Law firm searching 50,000 case documents · Government querying all APS policy documents · Hospital searching clinical guidelines
Optimal hardware
RTX 4090 / RTX PRO 6000 / GB10
VRAM per model
Low — 2–8 GB
Bottleneck
CPU / storage speed, not GPU
GridMind fit
Excellent — any tier
Most customers say "training" when they mean "inference" — this is the most common misunderstanding
When a CEO or CTO says "we want to train our own AI", they almost always mean one of two things: (1) they want to run a pre-trained model that already exists (inference), or (2) they want to fine-tune an existing model on their documents (fine-tuning). Full training from scratch is extremely rare and extremely expensive. Clarify this early — it determines whether the customer needs a $20,000 Starter node or a $400,000 Enterprise.
The most important hardware constraint — VRAM determines everything
VRAM: why it matters and how to calculate how much you need
VRAM (Video RAM) is the memory on the GPU where the AI model lives during operation. The entire model must fit in VRAM — if it doesn't fit, it either fails to load or runs at a crawl using system RAM. This single constraint determines which GPU tier the customer needs.
VRAM required by model size (inference, most common use case)
ModelParametersFP32 (full)FP16 (half)INT8 (quant.)INT4 (quant.)Minimum GPU tierNotes
Llama 3.2 1B1 billion4 GB2 GB1 GB0.5 GBAny — even GB10Entry-level — customer service bots, simple Q&A
Llama 3.1 7B7 billion28 GB14 GB7 GB4 GBRTX 4090 (24 GB) at INT8Good quality general assistant. Most common inference workload.
Llama 3.1 13B13 billion52 GB26 GB13 GB7 GB2× RTX 4090 or RTX PRO 6000Better quality. Needs GPU bridging at FP16.
Llama 3.1 70B70 billion280 GB140 GB70 GB35 GBRTX PRO 6000 Server (96 GB INT8) or 4× RTX 4090High quality. VRAM is the constraint. 4× RTX 4090 = 96 GB covers INT8.
Llama 3.1 405B405 billion1,620 GB810 GB405 GB202 GB8× H100 (640 GB HBM3) at INT4Frontier quality. Minimum 8× H100 at INT4. H200 or B200 more comfortable.
GPT-4 class (est.)~1.76 trillion7,040 GB3,520 GB1,760 GB880 GBMultiple Sovereign RacksNot practical on any single deployment. Frontier AI providers only.
What is quantisation and why does it matter?
A model trained in FP32 (32-bit floating point) stores each weight as a 32-bit number. FP16 halves that to 16 bits, INT8 halves again to 8 bits, and INT4 halves once more to 4 bits. Each step roughly halves the VRAM needed. INT4 quantisation (using tools like llama.cpp, GGUF format, or NVIDIA TensorRT-LLM) allows a 70B parameter model to run on 4× RTX 4090 GPUs (96 GB total) that would otherwise need 280 GB at FP32. Quality loss from INT4 is typically less than 2% on most benchmarks — imperceptible in practice. This is why the RTX 4090 and RTX PRO 6000 are so powerful at the SMB tier — they can serve very large models at acceptable quality using quantisation.
VRAM for concurrent users — the key multiplier
VRAM is shared — the model loads once, all concurrent users share it
This is a common misunderstanding. If a 7B model needs 14 GB of VRAM, it uses 14 GB regardless of whether 1 user or 50 users are querying it simultaneously. What changes with more concurrent users is the KV cache (key-value cache) — temporary memory used to track each active conversation. Each concurrent user typically uses 0.5–2 GB of KV cache, depending on context length. Rule of thumb: VRAM needed = model size + (concurrent users × 1 GB KV cache). So 8 concurrent users on a 7B model = 14 GB + 8 GB = 22 GB. Just fits in an RTX 4090.
GridMind VRAM capacity by node
NodeTotal VRAMMax model (INT4)Max concurrent users (7B model)Max concurrent users (70B model)
Starter (4× RTX 4090)96 GB~190B parameters (INT4)~80 users~60 users (70B INT4 = 35 GB + 60 GB KV)
Spark (GB10)128 GB unified~250B parameters (INT4)~110 users~90 users
Pro (4× RTX PRO 6000)192 GB ECC~380B parameters (INT4)~170 users~155 users
Starter Plus (8× RTX 4090)192 GB~380B parameters (INT4)~170 users~155 users
Enterprise (8× H100 NVL)640 GB HBM2eLlama 3.1 405B (INT4)~600 users~550 users
Enterprise (8× H100 SXM5)640 GB HBM3Llama 3.1 405B (INT4)~600 users~550 users
Ent H200 (8× H200 SXM5)1.1 TB HBM3eMultiple 405B instances~1,050 users~1,000 users
Ent B200 (8× B200)1.5 TB HBM3eMultiple large models~1,450 users~1,400 users
Sovereign NVL7213.8 TB HBM3eMultiple frontier models13,000+ users13,000+ users
Software AI engineer perspective
Throughput vs latency — two different performance requirements
A hospital emergency system needs answers in <500ms (latency matters). A law firm processing 10,000 contracts overnight just needs high throughput — speed per document doesn't matter as much as volume. The GPU choice differs significantly between these two requirements.
Latency-critical (real-time) workloads
The user is waiting. Every 100ms matters. These workloads need fast time-to-first-token (TTFT) — the delay before the first word appears. HBM3e memory (H200, B200) has much higher bandwidth than GDDR6X (RTX 4090), which means faster initial response.
  • ICU patient monitoring — alert latency ≤ 500ms
  • Real-time fraud detection — decision in < 1 second
  • Voice AI assistants — response in < 200ms for natural feel
  • Trading / algorithmic decisions — sub-millisecond in some cases
Hardware priority: HBM3e memory bandwidth. H200 (4.8 TB/s) or B200 (8.0 TB/s) for smallest models at highest speed. RTX 4090 acceptable for <13B models.
Throughput-critical (batch) workloads
The user submits a job and comes back later. Speed per item doesn't matter — total volume per hour does. These can run at lower priority during off-peak hours (GSOL idle time). The GPU should process as many tokens per second as possible for maximum throughput.
  • Overnight document summarisation (law firm)
  • Bulk embedding generation — indexing 100,000 documents
  • Batch medical record coding / ICD classification
  • Model fine-tuning runs (hours to days)
Hardware priority: Raw TFLOPS. B200 at 9,000 TFLOPS FP4 is ideal. RTX 4090 clusters are cost-effective. GSOL idle time can fulfil batch jobs from other organisations.
Tokens per second by GPU (Llama 3.1 70B, INT4 quantisation)
GPU configurationTokens/sec (70B INT4)Words/sec (approx)User experience
4× RTX 4090 (96 GB)~18–25 tok/s~13–18 words/secReadable streaming — slightly slow for heavy users
8× RTX 4090 (192 GB)~35–50 tok/s~25–35 words/secGood streaming experience
4× RTX PRO 6000 Server (192 GB)~40–55 tok/s~28–40 words/secGood — better memory bandwidth than 4090
8× H100 NVL (640 GB)~120–150 tok/s~85–107 words/secExcellent — near-native typing speed
8× H100 SXM5 (640 GB)~150–200 tok/s~107–143 words/secExcellent — fast even for complex prompts
8× H200 SXM5 (1.1 TB)~200–280 tok/s~143–200 words/secVery fast — HBM3e 4.8 TB/s memory bandwidth
8× B200 (1.5 TB)~400–600 tok/s~285–430 words/secExceptional — 8 TB/s memory bandwidth
Figures are estimates for single-user inference. Throughput per user decreases with concurrency but total system throughput increases. Real-world figures depend on context length, prompt complexity, and batch size.
Why some GPUs need special cabling and some don't
GPU interconnect — NVLink vs PCIe, and when it matters
When a model is too large to fit on a single GPU, it must be split across multiple GPUs. How fast those GPUs can communicate with each other determines how efficiently the distributed model runs. The wrong interconnect can negate the benefit of having multiple GPUs.
PCIe (standard motherboard bus)
The standard connection between GPU and motherboard. PCIe 4.0 × 16 = 64 GB/s in each direction. Adequate for inference where GPUs don't need to talk much, but too slow for training where GPUs constantly synchronise gradients.
Bandwidth
64 GB/s (PCIe 4.0 × 16)
When sufficient
Inference · Embedding
When insufficient
Training · Large fine-tuning
GridMind nodes
RTX 4090 · RTX PRO 6000 · H100 NVL
NVLink (NVIDIA proprietary high-speed)
Direct chip-to-chip connection between NVIDIA GPUs. 10–28× faster than PCIe. Makes a cluster of 8 GPUs behave almost like a single giant GPU. Essential for training and large-model inference where the model must be split across multiple GPUs.
NVLink 4.0 (H100 SXM5)
900 GB/s bidirectional
NVLink 5.0 (B200)
1,800 GB/s bidirectional
NVLink-C2C (GB200)
900 GB/s CPU–GPU
GridMind nodes
H100 SXM5 · H200 SXM5 · B200 · GB200
InfiniBand — connecting multiple servers together
NVLink connects GPUs within one server. When you need multiple servers to cooperate on the same training job (distributed training across server nodes), you need a high-speed network. Standard 1 GbE or 10 GbE Ethernet is far too slow. All GridMind Enterprise nodes include InfiniBand HDR (200 Gb/s) or NDR (400 Gb/s) NICs. InfiniBand provides ~25 GB/s per port vs ~1.25 GB/s on 10 GbE — it's effectively the "PCIe" of the network layer. For inference-only GridMind deployments, standard 10 GbE is typically sufficient. InfiniBand only becomes critical when you're splitting a single training job across multiple servers.
AI software engineer perspective
The software stack — what runs on top of the hardware
Hardware is useless without the software that talks to it. Understanding the AI software stack lets you have informed conversations with technical teams and know what GSOL provides vs what the customer needs to bring themselves.
Layer by layer — from silicon to application
Customer application
Web interface, API, chatbot, document pipeline — what end users actually interact with
Customer builds this. GridMind provides compute only.
Inference server
vLLM, Ollama, NVIDIA Triton, llama.cpp — manages the model and handles concurrent requests via batching
GSOL deploys standard options. Customer can bring their own.
AI framework
PyTorch, JAX, TensorRT-LLM — the Python library that talks to the GPU driver and runs tensor operations
Pre-installed on all GridMind nodes via GSOL.
CUDA / ROCm
NVIDIA's parallel computing platform. Every AI framework uses it. Requires specific driver versions. All GridMind nodes run CUDA 12.x.
Managed by GridMind. Automatic updates via GSOL.
GPU driver + firmware
NVIDIA driver 535+ for Hopper, 560+ for Blackwell. Controls how the OS talks to the GPU silicon.
GridMind manages. Locked by GSOL. Not customer-accessible.
GPU silicon
RTX 4090 · H100 · H200 · B200 · GB200 — the physical chip doing the matrix multiplication
GridMind owns and maintains.
Key software the customer will likely ask about
SoftwareWhat it doesCustomer uses it whenGridMind stance
vLLMHigh-throughput inference server. PagedAttention means very efficient KV cache management — 2–4× more concurrent users per GPU than naive approaches.They need to serve many users from one GPU node simultaneouslyPre-deployed via GSOL. Default inference backend.
OllamaSimple local model runner. One command to download and run any open model. No coding required.Small team needs to run Llama/Mistral without DevOps complexityAvailable on Starter/Spark nodes. Customer self-service.
NVIDIA TensorRT-LLMNVIDIA's optimised inference compiler. Converts model to GPU-specific format for 2–4× speed increase vs raw PyTorch.Maximum performance on H100/H200/B200Available. Requires brief setup — GSOL can configure.
LangChain / LlamaIndexPython libraries that help build RAG pipelines — connecting LLMs to databases, documents, APIs.Customer is building a RAG system over their documentsCustomer installs themselves — no GPU interaction.
Hugging Face TransformersLibrary with 500,000+ pre-trained models and standard interface to load and run them.Customer needs a specific model for their use casePre-installed on all nodes. Standard interface.
NVIDIA AI Enterprise (NVAIE)NVIDIA's commercial support subscription. Includes security updates, SLAs, and enterprise support for AI frameworks.Government/APRA-regulated customers needing vendor support SLAAvailable on RTX PRO 6000 Server, H100, H200, B200 tiers.
The quick reference guide — for customer conversations
Hardware decision guide — from customer requirement to GridMind node
Use this guide in customer meetings. Ask these questions in order. The answers lead directly to the correct hardware tier without needing any engineering background.
The four questions to ask every customer
1
"Are you running an existing model, or training a new one?"
Existing model (inference) → almost any tier works. Training from scratch → Enterprise+ minimum. Fine-tuning → Enterprise or Pro minimum.
2
"Which AI model are you planning to use? Do you know its size in parameters?"
7B → Starter or Pro. 70B → Pro or Starter Plus. 405B → Enterprise or Enterprise. Bigger → H200, B200, Sovereign. If they don't know: ask "What task — answering questions, writing reports, analysing images?" and match from there.
3
"How many people will be using it at the same time, at peak?"
1–20 concurrent → Starter or Spark fine. 20–100 → Starter Plus or Pro. 100–500 → Enterprise or Enterprise. 500–2,000 → Ent H200 or B200. 2,000+ → multiple nodes or Sovereign.
4
"Do you have compliance requirements — IRAP, APRA, health data?"
Standard commercial → any tier. IRAP SENSITIVE / APRA → RTX PRO 6000 minimum (NVIDIA AI Enterprise). IRAP PROTECTED → Enterprise or H100 minimum (Phase 2 pathway). Defence classified → contact GridMind directly — sovereign configuration.
Quick lookup matrix
If the customer says...Workload typeRecommended nodeWhy
"We want a chatbot for our 200 staff"Inference, 7B–13B modelStarter or Pro200 staff rarely all concurrent. 20–50 peak → 96 GB VRAM sufficient for 13B INT8.
"AI tutor for our 1,000-student school"Inference, 7B model, many concurrent7–8× Starter Plus280 peak students × 1 GPU/8 users = ~35 GPUs. 7 Starter Plus = 56 GPUs + headroom.
"Replace our AWS Bedrock spend"Inference, Claude-class modelEnterprise or H200AWS Bedrock runs 70B+ class models. Needs 640 GB+ VRAM. H100 or H200.
"Train a model on our patient records"Fine-tuning or training, 7B–70BEnterprise or EnterpriseFine-tuning 70B needs ~280 GB VRAM. H100 NVL cluster (640 GB) covers this.
"Real-time fraud detection under 1 second"Inference, latency-criticalStarter Plus or ProFraud models are typically 7B–13B. VRAM is low. Latency is the driver — multiple GPUs for parallel queries.
"We process 50,000 documents overnight"Batch embedding or summarisationStarter or Starter PlusBatch, not real-time. RTX 4090 at 18–25 tok/s × 4 GPUs × overnight = millions of tokens. More than enough.
"Sovereign AI for ADF logistics"Inference + fine-tuning, classifiedSovereign Rack (NVL72)Defence workloads require IRAP PROTECTED, maximum performance, air-gapped capability.
AI-powered — acts as your senior computer and AI software engineer
Ask the AI hardware advisor
Describe your customer's situation in plain English. The AI advisor — drawing on the expertise of a senior computer engineer and AI software engineer — will specify the correct hardware, explain why, identify any risks, and give you a script for the customer conversation.
Describe the customer and their AI requirement
Consulting computer engineer and AI software engineer...

Hybrid Agentic Inference — The Inevitable Architecture

This is not a product pitch. It is a description of where every regulated Australian business is heading — whether they know it yet or not.

The fundamental argument
Your competitive advantage is not the AI model you chose.
It is the tacit knowledge your organisation has built over years —
and your ability to train AI on it.
The commodity
The frontier AI model
GPT-4, Claude, Llama, Gemini — available to every one of your competitors at the same price. The model itself confers no advantage. It is infrastructure, like electricity.
The moat
Your tacit knowledge
The clinical protocols refined over decades. The underwriting intuitions from thousands of claims. The engineering solutions from years of failure. The customer patterns no competitor can see. This cannot be bought. This is yours.
The advantage
AI trained on your tacit knowledge
A model fine-tuned on your proprietary data outperforms any frontier model on your specific domain tasks — because it knows your business from the inside. This runs on infrastructure you own. It never leaves.
Your competitive advantage is your knowledge. Your knowledge lives in your data.
Your data must stay on your hardware. That is why you own your AI infrastructure.
Why your data must never leave your infrastructure
The moment you send your proprietary data to a hyperscaler for AI processing, you have handed your most valuable asset to a company whose infrastructure also serves your competitors. You have no visibility into how that data is used. You have no guarantee it is not used to train their next model. And you have US CLOUD Act exposure on top of all of that.
A hospital's patient outcomes data is its most valuable research asset. A bank's fraud pattern data is its most defensible competitive moat. A manufacturer's quality control data is its accumulated engineering intelligence. These are not just compliance obligations — they are strategic assets that belong on infrastructure the organisation controls.
The direction of travel — legislation always follows practice
We legislated data sovereignty because nations recognised that sensitive national information should not be stored on foreign infrastructure. The same logic is now being applied to AI inference — because inference on data is equivalent to transmitting that data to whoever runs the compute.
GDPR came after data misuse was already happening. APRA CPS 230 came after the operational risk was already being taken. Privacy Act 2024 penalties are already law. The AI inference enforcement action hasn't landed yet — but when it does, every organisation sending sensitive data to offshore AI will need a solution in weeks. GridMind deploys in 12.
The hybrid architecture — two inference paths, one AI interface
Sensitivity routing — how every query is handled
Cloud frontier model (AWS / Azure / OpenAI)
General research · Public document summaries · Marketing content · Code assistance · Staff training · Any query with no sensitive content
Best capability · Lowest cost · Fine for public data
US CLOUD Act applies regardless of server location
AI Agent
Classifies sensitivity · Routes query · Combines result
GridMind on-premises (your hardware)
Patient records · Financial data · Employee files · Legal advice · Proprietary IP · Commercial negotiations · Classified briefs · Anything that cannot legally leave
Open source 70B model fine-tuned on your tacit knowledge
Australian hardware · Australian law · Your model · Your advantage
Do you need frontier models? — the open source question
Open source is sufficient for most sensitive workloads
Llama 3.1 70B, Mistral Large, Qwen 2.5, DeepSeek — running on a GridMind H100 cluster — handles clinical documentation, legal contract review, fraud alert analysis, policy drafting, and financial reporting at a quality that most enterprise users cannot distinguish from GPT-4 in practice.
For these workloads, you don't need a frontier model. You need a model that understands your domain — and a 70B open source model fine-tuned on your institutional data outperforms any general frontier model on your specific tasks, because it knows your business from the inside.
Where frontier models genuinely outperform open source
Complex novel reasoning chains · Multi-step scientific analysis · Broad creative tasks requiring world knowledge · Very long-context planning across many steps
Critically: these tasks almost never involve sensitive data. A lawyer asking for novel legal argumentation on a public case can safely use a cloud frontier model. The overlap between "needs frontier capability" and "contains sensitive data" is smaller than most people assume — which makes the hybrid architecture work cleanly.
The GSOL advantage — two benefits most customers don't expect
Benefit 1 — Idle capacity earns passive revenue

Your GridMind hardware runs your AI workloads during business hours. But outside those hours — nights, weekends, and quiet periods — the GPUs are idle. GSOL automatically sells that idle capacity on the GridMind sovereign marketplace, generating passive revenue for your organisation with zero management overhead.

A hospital with 7× Starter Plus nodes (56 GPUs) running AI during clinical hours has approximately 82% of total annual GPU-hours available for GSOL. At blended marketplace rates, that generates $67,000–$185,000 per year in passive income — offsetting electricity costs and partially recovering hardware investment.

You didn't buy the hardware just to run your own AI. You bought infrastructure that works for you 24 hours a day — your workloads during the day, GSOL revenue through the night.

Benefit 2 — Burst to sovereign hardware when demand spikes

Your own hardware is sized for your typical peak load — not for rare demand spikes. When your annual report drops and 800 staff hit the AI system simultaneously, or when a major clinical event requires intensive compute, your local nodes may reach capacity.

GSOL gives you access to the wider GridMind network — other organisations' idle sovereign hardware across Australia, all IRAP-pathway compliant, all on Australian soil. Your overflow queries route to the nearest available GSOL node automatically. You get burst capacity on demand, with the same sovereignty guarantees as your own hardware.

This is the key difference from cloud bursting. Cloud burst capacity goes to AWS or Azure — back to foreign-owned infrastructure, back to CLOUD Act exposure. GSOL burst capacity stays on Australian sovereign hardware, owned by other Australian organisations, governed by Australian law.

Pitching this in a customer conversation
Opening — reframe from compliance to strategy
"The question isn't whether you'll run AI on local sovereign infrastructure. That's already being legislated — just like data residency was legislated after Snowden. The question is whether you build that infrastructure now, as a strategic asset, or scramble to build it when an enforcement action forces your hand."
The tacit knowledge argument
"Your competitive advantage isn't the AI model you chose — every one of your competitors can buy the same model at the same price. Your advantage is the tacit knowledge your organisation has built over years. The clinical protocols, the risk models, the customer patterns, the institutional memory. The organisation that trains AI on that knowledge, on infrastructure they own, builds a compounding advantage that no competitor can replicate — because they don't have your data."
The question that closes the conversation
"Can you show me your current data governance policy for what categories of information you're allowed to send to AWS or Azure for AI processing?" — Most organisations either don't have one, or their policy explicitly prohibits the categories they're already sending. This creates the moment of realisation. GridMind is the solution that was already required.
The economics close
"And while your hardware is idle — nights, weekends, quiet periods — GSOL is selling that spare capacity and depositing revenue into your account. If you hit a demand spike, GSOL bursts you to sovereign Australian hardware from other organisations on the network. You're not just buying AI infrastructure. You're buying an asset that generates income and scales with you — on Australian soil, under Australian law, serving your competitive advantage."

Hardware Pricing — AUD cost reference

Complete bill of materials for every GridMind node tier. All prices in AUD inc. GST. Exchange rate: AUD/USD 0.70 (June 2026). Prices are indicative ranges — confirm with distributors before quoting customers.

SMB tiers (Starter, Spark, Pro, Starter Plus) — component assembly
RTX 4090, RTX PRO 6000 Blackwell Server Ed., and GB10 are PCIe cards or appliances — buy individually and assemble in a validated server chassis. Saves 25–35% vs pre-built. Sourceable via Ingram Micro AU, Arrow AU, Scorptec, PCCaseGear.
Enterprise tiers (H100, H200, B200, Sovereign) — complete Supermicro / Dell system only
H100/H200/B200 SXM GPUs are NOT sold individually — only as HGX baseboard assemblies inside OEM servers. You cannot buy the GPU chip alone. Supermicro SYS-821GE-TNHR and SYS-A21GE-NBRT-G1 are the required vehicles.
💻 Who this product is for
Individual developer · sole trader · startup · proof of concept. Zero installation — sits on a desk, plugs into a standard 10A power point. Connect to GSOL and earn passive revenue from idle capacity. The entry point — when they see it work, they upgrade to a Starter node.
Upgrade path
Upgrades to: GridMind Starter (outdoor unit)
NVIDIA Spark
Spark
NVIDIA Spark (GB10)
Hardware only
$6K–$9K
Enclosure (optional)
$2K–$3K
Total excl. install
$8K–$12K
Total incl. install
$9K–$14K
Build approach: Buy the NVIDIA Spark appliance complete — the GB10 superchip is not sold separately. USD $3,000–$4,000 estimated (AUD $4,300–$5,700 + 10% duty + GST ≈ $5,200–$7,000 landed). Source: NVIDIA direct or Ingram Micro AU. Lead times 8–14 weeks.
ComponentSpecQtyUnit (AUD)Total (AUD)AU supplier
NVIDIA Spark (GB10 superchip)128 GB unified · 450W · integrated system1$5,200–$7,000$5,200–$7,000NVIDIA AU / Ingram Micro AU
UPS 1 kVAAPC Back-UPS Pro 1500VA1$400–$600$400–$600APC AU
GSOL management SBCPi CM4 out-of-band1$120–$200$120–$200Core Electronics AU
10GbE managed switchTP-Link TL-SG2210P or equiv.1$250–$400$250–$400Amazon AU
IP55 outdoor housing (optional)Compact wall-mount enclosure1$1,500–$2,500$1,500–$2,500Custom AU fabricator
Total excl. installation$7,470–$10,700
Licensed electrician (10A dedicated)Circuit + cert · 2 hrs1$400–$700$400–$700Local electrician
Installation labour (1 hr)Mounting + network1$150–$300$150–$300GridMind
TOTAL INCL. INSTALLATION$8,020–$11,700
🏪 Who this product is for
Small business (10–50 staff) · GP clinic · law firm (small) · accountant · real estate agency · retail chain HQ. General AI workloads — email drafting, document summaries, client Q&A, compliance checking. No DA, no builder, no MCPU. Half-day installation.
Upgrade path
Upgrades to: GridMind Starter Plus (same enclosure, add 4 GPUs)
NVIDIA RTX 4090
RTX 4090
NVIDIA RTX 4090 24 GB
Hardware only
$18K–$22K
Enclosure + power
$3K–$5K
Total excl. install
$21K–$27K
Total incl. install
$24K–$31K
Build approach: Component assembly. RTX 4090 stock is thinning (discontinued). Buy in bulk from Ingram Micro AU or Scorptec now. Threadripper PRO 5955WX platform for the motherboard. Price: AUD $2,400–$3,200/card (retail), potentially rising as stock depletes.
ComponentSpecQtyUnit (AUD)Total (AUD)AU supplier
NVIDIA RTX 4090 24 GBAda Lovelace · 450W · GDDR6X4$2,400–$3,200$9,600–$12,800Scorptec / PCCaseGear / Ingram Micro
AMD Threadripper PRO 5955WX CPU16C · 280W · sTRX41$1,800–$2,400$1,800–$2,400MSY / PBTech AU
WRX80 workstation motherboardsTRX4 · PCIe 4.0 · 4× x16 slots1$1,200–$1,600$1,200–$1,600ASUS Pro WS WRX80E-SAGE
DDR5 ECC RDIMM 128 GB6× 32 GB DDR5-4800 ECC6$180–$240$1,080–$1,440Kingston / Samsung via Ingram
Samsung 990 Pro NVMe 4 TBPCIe 4.0 · 7,400 MB/s2$350–$450$700–$900Amazon AU / Scorptec
2× 1,200W 80+ Titanium PSUSeasonic / EVGA Supernova2$380–$500$760–$1,000Scorptec / PBTech
PCIe 4.0 riser cables × 4200mm right-angle ribbon4$80–$120$320–$480AliExpress / custom AU
GSOL ARM SBCRaspberry Pi CM4 management1$120–$200$120–$200Core Electronics AU
10GbE NICIntel X550-T11$220–$300$220–$300Server Parts AU
Hardware subtotal$15,800–$21,120
IP55 outdoor aluminium enclosure3mm 6061-T6 · powder-coated · C41$2,500–$4,000$2,500–$4,000Custom AU fabricator
400mm axial fan + PWM controllerIP55 · variable speed · Ebm-papst1$300–$500$300–$500Ebm-papst AU
IP55 weatherproof socket + conduitClipsal 56 series 20A1$150–$250$150–$250Clipsal / Rexel AU
Total excl. installation$18,750–$25,870
Concrete pad (150mm · 1.2×0.8m)20 MPa · M12 anchors1$400–$800$400–$800Local concreter
Licensed electrician (20A circuit)Circuit + socket + ESO cert1$800–$1,400$800–$1,400Local electrician
Site labour (2 people · 4 hrs)Delivery + anchor + network1$400–$600$400–$600GridMind
TOTAL INCL. INSTALLATION$19,950–$28,670
🏥 Who this product is for
Mid-market enterprise · hospital department · law firm (mid-size) · financial services · government agency. Enterprise-grade ECC memory, ISV certifications, 24/7 server-validated passive cooling. Same outdoor enclosure as Starter/Starter Plus — different cards, higher compliance capability.
Upgrade path
Upgrades to: GridMind Pro Plus (same enclosure, add 4 RTX PRO 6000 cards)
NVIDIA RTX PRO 6000 Blackwell Server Edition
RTX PRO 6000 Server Ed.
NVIDIA RTX PRO 6000 Blackwell Server Ed.
Hardware only
$65K–$84K
Enclosure + power
$4K–$6K
Total excl. install
$69K–$90K
Total incl. install
$72K–$97K
Build approach: Component assembly in validated 4U server chassis (Supermicro or HPE platform for passive PCIe GPUs). RTX PRO 6000 Server Ed. sourced via Ingram Micro AU, Arrow AU, or HPE enterprise — not available at retail. USD $8,000–$9,200/card = AUD $11,500–$13,200/card before GST and distributor margin. Confirm availability — not yet in mainstream AU stock as of June 2026.
ComponentSpecQtyUnit (AUD)Total (AUD)AU supplier
NVIDIA RTX PRO 6000 Blackwell Server Ed.96 GB GDDR7 ECC · 450W passive · PCIe 5.04$11,500–$13,500$46,000–$54,000Ingram Micro AU / Arrow AU / HPE AU
AMD Threadripper PRO 7995WX96C · 350W · sTR5 · Zen 41$8,000–$12,000$8,000–$12,000PBTech AU / PCCaseGear (price volatile)
WRX90 server motherboardsTR5 · 8× PCIe 5.0 slots · 12 DIMM1$1,800–$2,800$1,800–$2,800ASUS Pro WS TRX90-E2-SAGE SE
256 GB DDR5 ECC RDIMM12× 32 GB DDR5-5200 registered ECC12$200–$280$2,400–$3,360Kingston / Micron via Ingram AU
NVMe 4 TB × 2Samsung 990 Pro PCIe 4.02$350–$450$700–$900Scorptec
4U server chassis (passive GPU airflow)Front-to-back · Supermicro SC847 equiv.1$800–$1,400$800–$1,400Supermicro AU (ASI)
2× 2,000W 80+ Titanium PSU (redundant)Server-grade hot-swap2$600–$900$1,200–$1,800Seasonic / FSP via Server Parts AU
10GbE dual-port NICIntel X710-T2L1$400–$600$400–$600Server Parts AU
GSOL ARM SBCRaspberry Pi CM41$120–$200$120–$200Core Electronics AU
Hardware subtotal$61,420–$77,060
IP55 outdoor enclosure (Pro — larger unit)3mm 6061-T6 · dual PSU bay · fan array1$3,500–$5,500$3,500–$5,500Custom AU fabricator
Redundant axial fan array (2× 400mm)IP55 · Ebm-papst1$600–$1,000$600–$1,000Ebm-papst AU
Total excl. installation$65,520–$83,560
Concrete pad (1.5×1.0m · 150mm)20 MPa · M12 anchors1$600–$1,100$600–$1,100Local concreter
Licensed electrician (32A 3-phase)3-phase circuit + switchboard + cert1$1,500–$2,500$1,500–$2,500Local electrician
Site labour (4 hrs)Delivery + positioning + network1$600–$900$600–$900GridMind
TOTAL INCL. INSTALLATION$68,220–$88,060
🏢 Who this product is for
Growing SMB (20–100 staff) · school · medium professional firm · Starter customers who have outgrown 4 GPUs. Same outdoor enclosure as Starter — upgrade by adding 4 GPU cards to the existing unit. No new pad, no new electrician, no new installation.
Upgrade path
Upgrades to: GridMind Pro (enterprise-grade ECC cards)
Hardware only
$35K–$50K
UNIT-A1 SIP pod
$55K–$95K
Total excl. install
$90K–$145K
Total incl. install
$110K–$175K
Build approach: Component assembly (8× RTX 4090) in Supermicro 8-GPU server chassis (AS-4124GS-TNRT2 or 4124GO-NART). UNIT-A1 SIP panel pod for the enclosure. Procure hardware and pod separately — both ship to site and assemble independently on the concrete slab.
ComponentSpecQtyUnit (AUD)Total (AUD)AU supplier
NVIDIA RTX 4090 24 GBAda Lovelace · 450W · GDDR6X8$2,400–$3,200$19,200–$25,600Scorptec / Ingram Micro bulk
Supermicro AS-4124GS-TNRT2 chassis + board8-GPU · AMD EPYC · 8× PCIe 4.0 x161$4,000–$6,500$4,000–$6,500Supermicro AU (ASI)
AMD EPYC 9554 or 2× Xeon Platinum 8480+Server CPU · 360W TDP · dual socket2$3,500–$5,500$7,000–$11,000Ingram Micro AU
256 GB DDR5 ECC RDIMMServer platform memory1$1,800–$2,600$1,800–$2,600Kingston / Micron
NVMe 4 TB × 2 · switch · rack + PDUStorage + networking + rack infrastructure1$2,500–$4,000$2,500–$4,000Various AU
Hardware subtotal$34,500–$49,700
UNIT-A1 SIP panel pod (48m²)SIPs Industries WA · R3.8 · C4 · Class 10a1$40,000–$67,200$40,000–$67,200SIPs Industries WA / SIPs QLD
200mm RC slab (6×8m)N12 mesh · 32 MPa · 12 kN/m²1$8,000–$14,000$8,000–$14,000Local concreter
Total excl. installation labour$82,500–$130,900
Electrician (125A 3-phase) + pod erection + commissioning4-person crew · 2 days · GSOL live1$10,000–$18,000$10,000–$18,000QBCC builder + electrician
TOTAL INCL. INSTALLATION$92,500–$148,900
NVIDIA RTX PRO 6000 Blackwell Server Edition
NVIDIA RTX PRO 6000 Blackwell × 8
Hardware only
$128K–$165K
Enclosure + power
$4K–$7K
Total excl. install
$132K–$172K
Total incl. install
$136K–$180K
Build approach: Same as Pro — 8× RTX PRO 6000 Blackwell Server Edition in validated 4U passive GPU server chassis. This is the upgraded Pro enclosure — same outdoor unit, full GPU bay populated. Source via Ingram Micro AU or Arrow AU enterprise channels. Confirm stock availability — Server Edition is enterprise channel only.
ComponentSpecQtyUnit (AUD)Total (AUD)AU supplier
NVIDIA RTX PRO 6000 Blackwell Server Ed.96 GB GDDR7 ECC · 450W passive · PCIe 5.08$11,500–$13,500$92,000–$108,000Ingram Micro AU / Arrow AU / HPE AU
AMD Threadripper PRO 7995WX96C · 350W · sTR5 · Zen 41$8,000–$12,000$8,000–$12,000PBTech AU / PCCaseGear
WRX90 server motherboard + 256 GB DDR5 ECCsTR5 · 8× PCIe 5.0 · memory1$4,500–$6,500$4,500–$6,500ASUS Pro WS / Kingston via Ingram
4U server chassis + 2× 2,000W PSUPassive GPU airflow · Supermicro equiv.1$2,500–$3,800$2,500–$3,800Supermicro AU (ASI)
10GbE dual-port NIC + GSOL SBC + NVMeManagement + storage1$700–$1,000$700–$1,000Server Parts AU
Hardware subtotal$107,700–$131,300
IP55 outdoor enclosure (Pro size)3mm 6061-T6 · dual PSU bay · fan array1$3,500–$5,500$3,500–$5,500Custom AU fabricator
Redundant axial fan array2× 400mm · Ebm-papst1$600–$1,000$600–$1,000Ebm-papst AU
Total excl. installation$111,800–$137,800
Concrete pad + licensed electrician (32A 3-phase) + site labourFull installation1$3,000–$5,000$3,000–$5,000Local trades
TOTAL INCL. INSTALLATION$114,800–$142,800
🏗 Who this product is for
Large enterprise · university · hospital network · government department running frontier-class inference. Requires dedicated UNIT-A1 building pod — separate structure, DA required, 6–14 weeks. Supermicro or Dell complete validated system. MCPU-S liquid cooling.
Upgrade path
Upgrades to: GridMind Enterprise Plus H200 (UNIT-A2 dual-storey pod)
Complete server
$300K–$460K
Enclosure + MCPU
$70K–$115K
Total excl. install
$370K–$575K
Total incl. install
$420K–$650K
Build approach: H100 NVL PCIe cards can be individually sourced (USD $25,000–$30,000 each = AUD $35,700–$42,900) but a validated Supermicro or Dell server chassis is strongly recommended for 8-GPU NVLink configuration. At this tier the firmware integration complexity makes pre-validated systems worth the 15–20% premium over component assembly.
ComponentSpecQtyUnit (AUD)Total (AUD)AU supplier
Validated 8× H100 NVL server (Dell XE8545 / Supermicro)8× H100 NVL · dual EPYC · 1TB DDR5 · NVLink1$300,000–$455,000$300,000–$455,000Dell AU enterprise / Supermicro AU (ASI)
InfiniBand HDR switch (40-port)Mellanox QM8790 · 200G HDR1$18,000–$28,000$18,000–$28,000Ingram Micro AU
42U rack + PDU (3-phase 32A)APC NetShelter + PDU1$3,000–$5,000$3,000–$5,000APC AU
GSOL management server (1U)Out-of-band · IPMI · GSOL agent1$2,000–$3,500$2,000–$3,500Supermicro AU
Hardware subtotal$323,000–$491,500
UNIT-A1 Kingspan PIR pod (48m²)100mm PIR · R5.3 · Class 8 · C41$52,800–$86,400$52,800–$86,400Kingspan AU
200mm RC slab · RPEQ certN12 · 32 MPa · 12 kN/m²1$12,000–$20,000$12,000–$20,000RPEQ structural contractor
MCPU-S dry cooler (rooftop)Vertiv CoolChip · up to 15kW1$8,000–$14,000$8,000–$14,000Vertiv AU
Total excl. installation labour$395,800–$611,900
DA + electrician (200A 3-phase) + pod erection + commissioningClass 8 approval · RPEQ · 5-day crew1$28,000–$50,000$28,000–$50,000QBCC builder + RPEQ electrician
TOTAL INCL. INSTALLATION$423,800–$661,900
Supermicro SYS-821GE-TNHR H100 8GPU server
Supermicro SYS-821GE-TNHR
Supermicro SYS-821GE-TNHR
Complete server
$453K–$570K
Enclosure + MCPU
$75K–$130K
Total excl. install
$528K–$700K
Total incl. install
$580K–$790K
Mandatory: buy complete Supermicro SYS-821GE-TNHR system. H100 SXM5 GPUs are not sold as individual cards — only as part of HGX H100 baseboard assemblies inside OEM servers. USD $317,495 list = AUD ~$453,500. Source via Supermicro AU distributor: ASI (Australian Scientific Instruments) or contact Supermicro AU directly.
ComponentSpecQtyUnit (AUD)Total (AUD)AU supplier
Supermicro SYS-821GE-TNHR (H100)8× H100 SXM5 · dual Xeon Platinum · 2TB DDR5 · 8× IB NDR1$453,500–$570,000$453,500–$570,000Supermicro AU (ASI) / Dell AU enterprise
InfiniBand NDR spine switchNVIDIA QM9790 · 40-port · 400G1$28,000–$45,000$28,000–$45,000Ingram Micro AU
42U rack + 3-phase PDU × 2APC NetShelter SX + APC AP89651$6,000–$10,000$6,000–$10,000APC AU
GSOL management server (1U)Out-of-band management · GSOL agent1$2,500–$4,000$2,500–$4,000Supermicro AU
Server + networking subtotal$490,000–$629,000
UNIT-A1 Kingspan PIR pod (48m²)100mm PIR · R5.3 · Class 8 · C4 · RPEQ1$52,800–$86,400$52,800–$86,400Kingspan AU
200mm RC slab · RPEQ certN12 · 32 MPa · 12 kN/m²1$14,000–$22,000$14,000–$22,000RPEQ structural contractor
MCPU-S rooftop dry coolerVertiv CoolChip · 15kW · 45°C coolant1$9,000–$16,000$9,000–$16,000Vertiv AU
Total excl. installation labour$565,800–$753,400
DA + RPEQ cert + electrician (250A 3-phase) + pod erection + commissioningClass 8 approvals · RPEQ · 6-day crew1$38,000–$62,000$38,000–$62,000QBCC builder + RPEQ licensed trades
TOTAL INCL. INSTALLATION$603,800–$815,400
🏢 Who this product is for
Large enterprise · national institutions · defence agencies. UNIT-A2 dual-storey pod. Direct liquid cooling mandatory.
Upgrades to: GridMind Enterprise Plus B200
Complete server (H200)
$453K–$640K
UNIT-A2 + MCPU
$150K–$260K
Total excl. install
$603K–$900K
Total incl. install
$680K–$1.02M
Mandatory: Supermicro SYS-821GE-TNHR configured with H200 HGX baseboard. Same chassis as H100 — different GPU baseboard (~15–20% premium). DLC mandatory. UNIT-A2 dual-storey pod required for H200 deployment (higher floor load rating, MCPU-M integration).
ComponentSpecQtyUnit (AUD)Total (AUD)AU supplier
Supermicro SYS-821GE-TNHR (H200)8× H200 SXM5 · 1.1TB HBM3e · DLC mandatory1$453,500–$640,000$453,500–$640,000Supermicro AU (ASI)
IB NDR switch + rack + PDU + GSOL mgmtNetworking + infrastructure1$36,000–$60,000$36,000–$60,000Ingram Micro AU / APC AU
MCPU-S or MCPU-M CDUVertiv · up to 60kW · 45°C coolant loop1$18,000–$30,000$18,000–$30,000Vertiv AU
Hardware subtotal$507,500–$730,000
UNIT-A2 Kingspan dual-storey pod150mm PIR · R8.0 · Class 8 · C4 · RPEQ1$110,000–$192,000$110,000–$192,000Kingspan AU + SHS fabricator
300mm PT RC slab (upper floor)N16 · 40 MPa · 20 kN/m²1$22,000–$38,000$22,000–$38,000RPEQ structural contractor
Total excl. installation labour$639,500–$960,000
DA + RPEQ + 400A 3-phase switchboard + pod erection + commissioningFull Class 8 dual-storey works · crane1$76,000–$120,000$76,000–$120,000QBCC builder + RPEQ licensed trades
TOTAL INCL. INSTALLATION$715,500–$1,080,000
🏛 Who this product is for
National AI agencies · top-tier research institutions · large defence. Maximum Blackwell performance. UNIT-A2 dual-storey pod.
Maximum capability in this product line
Complete server (B200)
$715K–$1.0M
UNIT-A2 + MCPU-M
$210K–$380K
Total excl. install
$925K–$1.38M
Total incl. install
$1.05M–$1.58M
Mandatory: Supermicro SYS-A21GE-NBRT-G1 (10U B200 Gold Series). USD $500,000+ = AUD $715,000+. Lead times 8–20 weeks. B200 HGX baseboard not sold as individual GPUs. DLC-2 mandatory — integrated into the 10U chassis.
ComponentSpecQtyUnit (AUD)Total (AUD)AU supplier
Supermicro SYS-A21GE-NBRT-G1 (B200)8× HGX B200 · 1.5TB HBM3e · 10U · DLC-2 · 400GbE1$715,000–$1,000,000$715,000–$1,000,000Supermicro AU (ASI) · 8–20 wk lead time
IB NDR400 switch + rack + PDU + GSOLNVIDIA QM9790 + 42U rack + 63A PDU1$48,000–$80,000$48,000–$80,000Ingram Micro AU / APC AU
MCPU-M CDU outdoor (60kW)Vertiv MegaMod HDX · outdoor dry cooler1$45,000–$75,000$45,000–$75,000Vertiv AU
Hardware subtotal$808,000–$1,155,000
UNIT-A2 Kingspan dual-storey pod (120m²)150mm PIR · R8.0 · Class 8 · C4 · RPEQ1$132,000–$216,000$132,000–$216,000Kingspan AU
300mm PT slab + civil + MCPU-M padRPEQ designed · 20 kN/m²1$55,000–$90,000$55,000–$90,000RPEQ structural contractor
Total excl. installation labour$995,000–$1,461,000
DA + RPEQ + 400A switchboard + crane + pod erection + DLC-2 + commissioningFull Class 8 · crane · 10-day crew1$97,000–$165,000$97,000–$165,000QBCC builder + RPEQ licensed trades
TOTAL INCL. INSTALLATION$1,092,000–$1,626,000
NVIDIA GB200 NVL72 sovereign rack
GB200 NVL72
NVIDIA GB200 NVL72
NVL72 rack hardware
$17M–$23M
UNIT-A2 + MCPU-L
$300K–$550K
Total excl. install
$17.3M–$23.6M
Total incl. install
$17.7M–$24.0M
NVIDIA AU direct or authorised partner only. USD $12M–$16M/rack = AUD $17.1M–$22.9M. Lead time 6–18 months. Requires NVIDIA country export approval. For Australian government or defence: contact NVIDIA AU + DTA for sovereign pathway. MCPU-L (LiquidStack GigaModular, 14MW scalable CDU) is the external cooling system.
ComponentSpecQtyUnit (AUD)Total (AUD)AU supplier
NVIDIA GB200 NVL72 rack system72× B200 · 36× Grace ARM · 13.8TB HBM3e · 120kW · DLC-21$17.1M–$22.9M$17.1M–$22.9MNVIDIA AU direct / Supermicro / Dell AU
IB NDR400 spine fabric (rack-integrated)Quantum-2 NVSwitch · NVLink 5.0 full mesh1$50,000–$90,000$50,000–$90,000Included / Ingram Micro AU
MCPU-L outdoor fluid podLiquidStack GigaModular · up to 140kW CDU1$120,000–$220,000$120,000–$220,000LiquidStack AU
GSOL management cluster (3-node redundant)Out-of-band · IPMI · GSOL sovereign agent1$15,000–$25,000$15,000–$25,000Supermicro AU
Hardware subtotal$17.3M–$23.2M
UNIT-A2 sovereign pod (120m²)150mm PIR · R8.0 · Class 8 · C4 · RPEQ cert1$132,000–$216,000$132,000–$216,000Kingspan AU
300mm PT slab + MCPU-L civil worksRPEQ · 20 kN/m² · high-spec civil1$60,000–$110,000$60,000–$110,000RPEQ structural contractor
Total excl. installation labour$17.5M–$23.5M
DA + Class 8 RPEQ + 1,000A switchboard + NVL72 specialist install + MCPU-L + commissioningNVIDIA certified install team · crane · 2-week programme1$225,000–$500,000$225,000–$500,000NVIDIA cert team + QBCC + RPEQ trades
TOTAL INCL. INSTALLATION$17.7M–$24.0M
For investor conversations
Is buying hardware and depreciating over 5 years better than paying hyperscalers forever?
Short answer: yes, decisively — and the advantage compounds over time. Here is the analysis a VC or CFO needs to see.
Configure scenario
Hardware tier
Peak usage hours/day 10 hrs
Number of GPUs in deployment 8
Depreciation period (years)
Annual hardware value decline
AWS monthly cost
$0
at peak usage hours
GridMind monthly cost
$0
depreciation + electricity only
Monthly saving
$0
vs staying on AWS
Payback period
0 yrs
hardware cost ÷ monthly saving
5-year AWS spend
$0
total outlay, zero residual
5-year GridMind TCO
$0
hardware + power (net of GSOL)
5-year net advantage (GridMind)
$0
savings vs AWS over 5 years
Annual depreciation (straight-line)
$0
P&L charge per year (non-cash after year 1)
Configure the scenario above to see the analysis.
Year-by-year comparison
YearAWS cumulative spendGridMind hardware cost (depreciated)GridMind electricityGridMind GSOL revenue (idle)GridMind net TCOCumulative advantage
Answering the VC / CFO questions
"Isn't it risky to spend $700K upfront when you could just use AWS and scale as needed?"
The risk framing is backwards. Every dollar spent on AWS is gone — zero residual value, zero asset, zero balance sheet contribution. Every dollar spent on GridMind hardware is a depreciating asset that appears on the balance sheet, generates GSOL revenue when idle, reduces monthly opex by the AWS equivalent, and retains some resale value. The AWS "flexibility" argument only holds if the organisation is uncertain whether they will need AI compute at all — and at the scale of an enterprise H100 deployment, that uncertainty has already been resolved. The risk of AWS is not flexibility — it is permanent operating leverage with no asset to show for it.
"What about hardware obsolescence — will a $700K H100 server be worthless in 5 years?"
Partially, but this cuts both ways. The H100 will still run inference on 70B models competently in 2030 — the models are not going to suddenly require more compute for the same tasks. What changes is that newer models may require more. The correct response is a refresh cycle, which every tech company already budgets for. More importantly: the comparison is not "H100 in 2030 vs B200 in 2026." The comparison is "H100 depreciated over 5 years vs the equivalent AWS spend over 5 years." The AWS equivalent spend is $0 in residual value and $0 in balance sheet contribution — the H100 at year 5 still has a resale market and still runs inference workloads that existed at year 1.
"How is the depreciation structured — is it deductible?"
In Australia, server hardware qualifies for depreciation under the general depreciation rules (Division 40 ITAA 1997). The ATO effective life for computer hardware is 5–6 years, making a 5-year straight-line schedule appropriate. Under the instant asset write-off provisions (current threshold AUD $20,000 for small business, full expensing for eligible businesses), a business may be able to deduct the full cost in Year 1 rather than depreciating — which significantly improves the Year 1 cash position. For enterprise-scale deployments ($700K+), standard Division 40 depreciation applies. The depreciation charge is a non-cash P&L item from Year 2 onwards — the cash outflow is at acquisition. Confirm treatment with your tax adviser. Note: this is information, not tax advice.
"What's the GSOL revenue assumption — is it realistic?"
The GSOL revenue in the model uses Vast.ai floor rates (H100: ~$2.50–$3.50/GPU-hr) as a conservative proxy for sovereign GPU marketplace pricing. The idle assumption (24 hrs minus peak usage hrs per day) is conservative — organisations typically use AI infrastructure for 8–12 hours of peak demand, leaving 12–16 hours per night available. GSOL revenue is presented as a partial offset rather than the primary financial case — even at zero GSOL revenue, the depreciation model substantially outperforms AWS over 5 years at enterprise scale. GSOL improves the case further but is not required for the model to work.
All tiers — AUD cost summary
NodeGPUs / VRAMBuild approachExcl. installIncl. installKey constraint
SparkGB10 · 128 GB unifiedBuy Spark appliance$7K–$11K$8K–$12KSpark only via NVIDIA/Ingram
Starter4× RTX 4090 · 96 GBComponent assembly$19K–$26K$20K–$29KRTX 4090 stock depleting
Starter Plus8× RTX 4090 · 192 GBComponent assembly$93K–$145K$110K–$175KRTX 4090 stock + UNIT-A1 pod lead time
Pro4× RTX PRO 6000 Server · 192 GBComponent assembly$66K–$84K$68K–$88KServer Ed. not in retail — enterprise channels only
Pro Plus8× RTX PRO 6000 Server · 384 GB ECCComponent assembly$112K–$138K$115K–$143KSame enclosure as Pro — upgrade by adding 4 cards
Enterprise8× H100 NVL · 640 GBValidated server recommended$400K–$615K$425K–$665KFirmware complexity — use Supermicro/Dell
Enterprise H2008× H200 SXM5 · 1.1 TBComplete Supermicro ONLY$640K–$960K$716K–$1.08MH200 SXM5 not sold individually · DLC mandatory
Enterprise B2008× B200 · 1.5 TBComplete Supermicro ONLY$995K–$1.46M$1.09M–$1.63M8–20 week lead time · DLC-2 mandatory
Sources: AUD/USD rate 0.70 (June 2026, Trading Economics). Supermicro SYS-821GE-TNHR USD $317,495 (Supermicro store). RTX PRO 6000 Blackwell USD $8,000–$9,200 (Thunder Compute, June 2026). H200 server +15–20% premium over H100 (industry consensus). B200 server USD $500,000+ (Supermicro/Viperatech). GB200 NVL72 USD $12M–$16M (NVIDIA). All AUD prices include 10% GST and estimated distributor margin. Installation costs are Queensland estimates — vary by site and region.

Customer Discovery — What hardware does this customer need?

Two paths to a hardware recommendation. Use whichever matches the conversation. Both paths output a tier recommendation, payback period, and a ready-to-use customer pitch.

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Contract review, research, drafting
Clinical / medical
Clinicians, radiologists, nurses
Developers / engineers
Copilot, code review, architecture
Research / science
Literature review, data analysis, lab AI
Customer support
Helpdesk, chatbots, ticket triage
Total staff
0
Tokens/day
Peak concurrent
Sensitive data categories (tick all that apply)
Enter staff headcount by role to see recommendation.
Staff headcount by role
Role type
Staff
AI intensity
Knowledge workers
Managers, admin, HR, finance, sales — email drafting, document summaries, Q&A
Customer-facing / sales
Real-time AI assist, live chat, proposal generation — latency sensitive
Lawyers / compliance / policy
Long document review, contract analysis — high context length, sensitive data
Clinicians / health workers
Clinical notes, patient summaries — sensitive, MUST be on-premises
Software developers / engineers
Code generation, code review, architecture — high throughput, long context
Researchers / analysts
Literature review, data analysis, report generation — very high token volume
Customer service / support
Chatbot / ticket triage — high request volume, short interactions
Total staff
0
Est. tokens/day
Peak concurrent users
Sensitive data involved?
Enter staff headcount by role on the left to see the hardware recommendation.

Contacts

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Categories
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