AI inference where you need it.

One platform for AI on your own hardware. Cloud to air-gapped.

Talk to the team How it works

[ Platform ]

One platform. Every environment.

One install. Any hardware. Any network. We show up and make it work.

Runs on any hardware

GPU, CPU, TPU, or mixed. Runtime probes the box and configures itself for what is there. A CPU-only field server, a single Jetson at a ground station, or a rack of H100s in a SCIF. Same install, same API.

Deploys to any environment

Cloud, on-prem, edge, air-gapped, or fully disconnected. Install over a clean network or an empty one. Same Runtime. Same Hub. Same API.

Stays on your side of the wire

Your data never leaves your network. Zero egress. No metered tokens. Prompts, responses, weights, and traces stay on the hardware you installed on.

Ground station at dusk with satellite dishes and radomes

Fig 1.1

Runtime

Probes the box, picks the engine, tiers memory, serves an OpenAI-compatible API. One install. Any hardware.

Explore Runtime

Fig 1.2

Hub

One control plane for every node. Deploy, monitor, hot-swap, and roll back across your entire fleet.

Explore Hub

Fig 1.3

Forward-Deployed Engineers

Audit, install, benchmark, harden. Our engineers embed in your team until you are live in production.

Explore FDE

Hardware Agnostic

Any GPU, any backend, any model, anywhere.

Hardware Platforms

NVIDIA CUDA
Popular
AMD ROCm
Intel Gaudi / Xeon
Google TPU
Qualcomm AI
Apple Silicon
CPU Servers
View all supported hardware →

Inference Backends

PyTorch Supported
Native inference
vLLM Supported
PagedAttention optimization
llama.cpp Supported
GGUF models, CPU/GPU
TensorRT-LLM Roadmap
NVIDIA optimization
Triton Roadmap
NVIDIA inference server
Ollama Roadmap
Developer tooling
View all supported backends →

Technology

How it works under the hood

Memory orchestration, engine selection, and the layers that hold it together.

Inside the platform

Benchmarks

Cited engine numbers, hardware envelope

Throughput across vLLM, TensorRT-LLM, and llama.cpp. Real reference deployment numbers.

See benchmarks

Hardware

Compatibility matrix

NVIDIA, AMD, Intel, Apple Silicon, ARM, CPU-only. What runs on what, with sizing per model class.

Open the matrix

Run it on your own hardware.

Bring in our forward-deployed engineers, or install it yourself.

Talk to the team