Docs · Bring your own model
Your weights. Our fleet.
Any open-weights checkpoint, one spec file, OpenAI-compatible endpoint out. Custom Docker servers supported for exotic stacks.
runaii.yaml
# runaii.yaml — model packaging spec (Truss-inspired, minimal) model: Qwen/Qwen3-32B engine: vllm # vllm | sglang | tgi | custom quant: nvfp4 # fp8 | nvfp4 | bf16 | awq | gptq context: 131072 gpus: min: 1 max: 8 types: [b200, b300] # scheduler picks cheapest fitting env: TENSOR_PARALLEL: "auto" endpoints: - /v1/chat/completions # OpenAI-compatible, always exposed healthcheck: path: /health ready_contains: "ok"
1. Package
Write runaii.yaml next to your weights (HF repo, GCS, or upload). We validate the spec in CI before a single GPU spins.
2. Deploy to staging
One command or console click. Staging gets its own endpoint for evals — priced at standard dedicated rates.
3. Promote
Traffic-split promotion with automatic rollback on error-rate or latency SLO breach. Zero downtime by construction.
Engine support
vLLM and SGLang first-class (tensor + pipeline parallel auto-configured). TGI supported. Custom servers: expose /health and OpenAI-schema endpoints on port 8000 — we handle TLS, autoscaling, and metering.