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Portability & Mobility
The mission can move, and the compute moves with it:
- Take your data and processing with you wherever you go
- Bring datacenter-class compute to places with poor and/or expensive connectivity
- Travel in challenging environments with a ruggedized case
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Configurability & Composability
No new hardware purchase, no re-engineering — just rearrange the sleds:
- Six different sleds for GPU, compute, storage, and networking
- Share storage or GPUs across multiple server sleds or VMs
- Open platform built for any accelerator (GPUs, ASICs, inference cards)
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Serviceability & Availability
Dramatically improve operational availability in environments without on-site support:
- If something fails, simply swap sleds in minutes
- Completely self-service — no technical expertise necessary
- No downtime even if technicians are days or weeks away
| PacStar MDCCurtiss-Wright | VoyagerHPCAnduril Klas VoyagerHPC + GPU 4.0 | EdgelineHPE EL2000 with EL220/240 Blades | GryfGigaIO | SourceCode★ Winning Solution | |
|---|---|---|---|---|
| Slots per Chassis | 4 | 1 | 2 | 6 |
| Max CPU Coressingle unit | Intel 16-core Processor | AMD EPYC™ 64-core Processor | Intel Xeon ProcessorScalable to 128 cores per slot | AMD EPYC™ 64-core ProcessorUp to 64 cores per slot |
| GPU Tier / Model | Workstation-classNVIDIA RTX 5000 Quadro PacStar MDC 2.0; 1 GPU per module | Workstation-classNVIDIA Ampere A4500 Voyager GPU 4.0 |
Virtual Desktop FocusedNVIDIA RTX6000 Server ProOne GPU only | NVIDIA RTX 6000 Server Pro 96GB, L40S 48GB or H100 NVL 94GBUp to 5 GPUs per unit |
| Total System Power | ~250–500 W | ~750 W | 1,300 WDual hot-plug PSUs | 2,500 WDual hot-swap PSUs |
| Max Storagesingle unit | Up to 32 TB | Up to 32 TB | Up to 60 TB/blade | Up to 5PBUp to 1PB per sled x 5 sleds |
| Best Fit For | Command posts, multi-enclave C2 | Expeditionary forces, tactical networks, vehicle integration | Industrial edge, telecom, defense | Large-scale AI inference, ISR analytics, petabyte-scale field processing |
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AI at Warp Speed
Bring processing power to where it’s needed — run AI inferencing and ML workloads where the data is being generated.
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Up to 10x Compute Capability
2,500 W power capacity provides up to 10x that of competitors offering only 250-1,500 W.
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Run 100 Billion Parameters
Access up to 5 datacenter-class GPUs in a single server to train larger AI models.
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Up to 5PB of Storage
Add storage sleds for an almost limitless storage capacity when needed.
Use Cases
Defense & Intelligence
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