# AI inference where you need it. One platform for AI on your own hardware. Cloud to air-gapped. ## 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. ### Runtime Probes the box, picks the engine, tiers memory, serves an OpenAI-compatible API. One install. Any hardware. ### Hub One control plane for every node. Deploy, monitor, hot-swap, and roll back across your entire fleet. ### Forward-Deployed Engineers Audit, install, benchmark, harden. Our engineers embed in your team until you are live in production. ## Hardware Agnostic Any GPU, any backend, any model, anywhere. ### Hardware Platforms ### Inference Backends ### How it works under the hood Memory orchestration, engine selection, and the layers that hold it together. ### Cited engine numbers, hardware envelope Throughput across vLLM, TensorRT-LLM, and llama.cpp. Real reference deployment numbers. ### Compatibility matrix NVIDIA, AMD, Intel, Apple Silicon, ARM, CPU-only. What runs on what, with sizing per model class. ## Run it on your own hardware. Bring in our [forward-deployed engineers](/forward-deployed), or install it yourself.