On-premises AI inference, operated as a service.
NVIDIA GPU nodes, a managed software stack, and remote operations. Deployed as a portable unit or a containerized cluster on your network. We run it. Your data and models stay on hardware you own.
Two form factors, one software stack.
Build on the portable unit and scale to a cluster without changing your application.
- 2 to 4 NVIDIA Blackwell-generation GPUs
- Up to 384 GB VRAM; 70B models on a single GPU
- ~2.9 to 5.8 PFLOPS FP8
- Standard 15 to 20A wall outlet, ~1 kW
- Operational 60 seconds after power-on
- 2 to 6 nodes, 16 to 48 GPUs
- Up to ~4.6 TB VRAM; ~23 to 70 PFLOPS FP8
- Power, cooling, fire suppression, physical security integrated
- 100A 208V 3-phase; commissioned in 12 to 14 weeks
- Larger containerized builds beyond 100 PFLOPS available
The same managed stack on every unit.
Built on standard, open components. No proprietary runtime to lock you in. A full inference platform, not just a model server, from the API your apps call down to the silicon.
The specifics on every unit:
- Inference serving standard HTTP and gRPC endpoints; models compiled for the GPUs they run on.
- Multi-node scheduling add a node and its capacity joins the pool, no reconfiguration.
- Distributed storage fault-tolerant NVMe across nodes, erasure-coded.
- Interconnect low-latency RDMA fabric between nodes.
- Workloads bring your own GPU-aware containers.
- Telemetry GPU, throughput, and health metrics across the fleet.
- Access an out-of-band management link you control, with optional site-to-site VPN.
Open weights, on hardware you own.
- Run open models you control: Llama, DeepSeek, Qwen, Mistral, and others.
- Right-size per workload: 7B to 70B on a single GPU, up to 405B tensor-parallel across four.
- Quantized serving: FP8 or INT4 where accuracy allows, for more throughput per GPU at lower power draw.
- Swap models without application changes; the inference endpoints stay the same.
- Fixed compute cost: you own the hardware, so inference is not metered per token.
We run it. Nothing leaves your building.
- Managed remotely monitoring, security patching, updates, and support.
- Zero data egress all inference runs on-premises, with no third-party API in the path.
- Model-layer security guardrails on inputs and outputs, plus red-team testing of prompts and grounding before a model goes live.
- Compliance aligns with PIPEDA and Québec Law 25, supports a SOC 2 posture, maps to NIST AI RMF.
- Hardware NVIDIA-certified components with enterprise warranty.
From proof of concept to production.
A demo is easy; an AI system your business runs on is hard. We build every step against the production target, so nothing gets thrown away, and the whole path takes months, not years.
Prove it on real data.
We stand up your highest-value use case on a portable unit and validate it against your own private data, with no cloud round-trip.
Days to first inferenceHarden and measure.
Right-size the models, add guardrails, wire it into your stack, and measure cost and latency under real load.
WeeksScale on owned hardware.
The same stack scales from a single unit to a containerized cluster. We operate it remotely; you own it outright.
In production within ~6 monthsOn-prem inference, operated as a service.
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