To fill this gap, we built a deployable AI data center that operates anywhere — no cloud, no compromise.
| Capability | MVP (Now) | vs. Market |
|---|---|---|
| AI Compute | 4x NVIDIA L40S | Single/no GPU |
| GPU Memory | 192 GB | 8-24 GB |
| Performance | ~6.5 PFLOPS | 0.01-0.1 |
| Storage | 50 TB NVMe | 1-10 TB |
| Connectivity | WiFi 6E + LTE | WiFi only |
| Power Draw | ~8 kW | Generator/shore |
| Runtime | 149+ hours | 2-4 hrs battery |
| Cooling | Immersion + Liquid + Air | Air only (0-35°C) |
| Revenue Stream | Description | Margin |
|---|---|---|
| Hardware Sales | Atlas @ $3.0M (commercial) | 81% |
| Support Contracts | Annual maintenance (15%) | 85%+ |
| Tech Refresh | GPU/storage upgrades (4-yr cycle) | 50%+ |
| Software Subs | NVIDIA AI Enterprise + mgmt | 90%+ |
| Compute-as-a-Svc | $/GPU-hr managed (OpEx) | 70%+ |
| Managed Services | Turnkey deploy & AI model mgmt | 85%+ |
| Metric | Y1 | Y2 | Y3 | Y4 | Y5 |
|---|---|---|---|---|---|
| Units Sold | 5 | 12 | 22 | 35 | 50 |
| Hardware Revenue | $15.0M | $36.0M | $66.0M | $105.0M | $150.0M |
| Services Revenue | $1.5M | $5.2M | $12.0M | $26.5M | $47.2M |
| Total Revenue | $16.5M | $41.2M | $78.0M | $131.5M | $197.2M |
| Gross Margin | 81% | 81% | 81% | 82% | 82% |
| Gross Profit | $13.4M | $33.4M | $63.2M | $107.8M | $161.7M |
| Capability | 3WM | Dell/HPE | Anduril | Klas/CW | Armada |
|---|---|---|---|---|---|
| Sovereign HW | ✓ | ✓ | Edge | ✓ | ✓ |
| Hostile Edge/Mobile | ✓ | Ltd | Ltd | ✓ | Ltd |
| DDIL Optimized | ✓ | ✓ | Part | Part | Part |
| Fuel Cell Power | ✓ | ✗ | ✗ | ✗ | ✗ |
| 149+ hr Runtime | ✓ | ✗ | ✗ | ✗ | ✗ |
| Immersion Cooled | ✓ | ✗ | ✗ | ✗ | Part |
| Cisco Native (85%) | ✓ | ✗ | ✗ | ✗ | ✗ |
| ~6.5 PFLOPS Mobile | ✓ | ✗ | ✗ | ✗ | ✓ |
| Private 5G Ready | ✓ | ✗ | ✗ | ✗ | ✗ |
3WM's defense-proven AI Pod deploys directly to commercial sectors where cloud connectivity is unavailable or sovereignty is critical.
| Advisor | Expertise | Affiliation | Contribution |
|---|---|---|---|
| Dr. Steven Storck | Material Science | JHU/APL | Custom thermal/radiator design |
| Chris Lyu | DoD Sales & Partnerships | Cisco DoD Sales Director | Channel access, tech validation |
| Ashish Aggarwal | Telecom AI Engineering | Cisco Engineer/Architect | HW optimization, validation |
| Swan Sit | Digital Transformation | Nike, Estée Lauder, Bain | Commercial expansion |
| Sarn Bien Aime | Small Business Mgmt | Sales & Market Development | Sales & market development |
| GPU & Compute | $83K | 6x GPUs, UCS C480/C240, DGX Spark |
| Networking & Security | $54K | N9K, C9300, C8300, FPR3110, ISE |
| Storage | $11K | 100TB NVMe |
| Power Systems | $151K | H2 fuel cell, EMILY backup, UPS |
| Cooling & Container | $80K | Immersion, radiator, HVAC, 10' ISO |
| Private 5G | $84K | AI-RAN, 3x O-RU, mast, 5G Core |
| Software & Licensing | $27K | NVIDIA AI Ent, Cisco, srsRAN |
| Integration & Testing | $40K | Assembly, burn-in, shipping |
| TOTAL (incl. 10%) | $583K | Per unit, all-in |
| Workload | Model | Performance |
|---|---|---|
| LLM Inference | Llama 3 70B, Mistral, Falcon | Real-time, <100ms |
| Computer Vision | YOLO, DETR, SegFormer | 30+ FPS video |
| Sensor Fusion | Multi-modal integration | Sub-second latency |
| Speech/NLP | Whisper, PersonaPlex | Real-time |
| RAG Systems | Document Q&A, KB | Enterprise-ready |
| Component | Power |
|---|---|
| 6x GPUs (L40S + RTX Pro) | 2,600W |
| UCS C480 + C240 M7 | 1,090W |
| DGX Spark (AI-RAN) | 240W |
| Cisco Networking (immersed) | 1,255W |
| Storage (100TB NVMe) | 164W |
| Cooling + HVAC | 1,720W |
| 5G Radios + PTP | 500W |
| Management/Misc | 110W |
| TOTAL SUSTAINED | ~7,960W |
| PEAK (GPU burst) | ~8,375W |
| Risk | Severity | Mitigation |
|---|---|---|
| GPU Supply | Medium | L40S not allocation-constrained; Cisco UCS multi-vendor compatible |
| Gov't Timeline Slippage | Medium | Signed LOI + funded pilot; parallel SBIR/DIANA paths |
| Power Gap (Fuel Cell vs Load) | Medium | Modular EFOY H2 (10kW, scalable 50kW); EMILY backup |
| Competitive Entry | Low-Med | DoD relationships; signed pilot/LOIs; patent protection |
| Technical Execution | Medium | JHU/APL thermal; Cisco proven UCS platform |
| ATO Timeline Uncertainty | High | Designed for ATO from inception; Cisco security-on-silicon |
| Small Team | Medium | Lean by design — integrators, not manufacturers; capital → product |