The inference platform for GPUs you own

One platform to serve, route and meter models on NVIDIA, AMD or any accelerator.

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Runs on
  • NVIDIA
  • AMD
  • Intel
  • Apple silicon
The InferOS dashboard: running inference deployments, router pools, GPUs in use, nodes and GPU-hour usage over the last seven days.
The InferOS dashboard on a live cluster.

Four products, one platform

Use them together, or start with the open-source parts.

Everything between your GPUs and your apps

OpenAI-compatible, so apps switch with one line of code.

Every request type
Chat, embeddings, images, speech, realtime and video, through one API.
Your GPUs and clouds
Your own models next to OpenAI, Anthropic and Gemini, with your keys.
Smart load balancing
Routes by latency target and by which replica already holds the context.
Built-in resilience
Failover, circuit breakers and response caching on every route.
Scoped API keys
Model allowlists, expiry, rate limits and spend caps per key.
Teams and tenants
Accounts, invites, and plans with their own limits.
Sign-in
Google, email codes and passkeys.
Usage per request
Tokens and cost on every call, plus GPU hours, ready for chargeback.
Dashboards and alerts
Built-in metrics with latency alerts to Slack.
Evals on live traffic
Scheduled quality checks and model judges on the models you serve.
Bring any machine
One install script enrolls Linux machines with NVIDIA or AMD GPUs, or CPUs.
Fit check first
A model that won’t fit is refused before it can crash a GPU.
Safe deployments
Versioned revisions, warm-up checks, graceful drains and rollback.
Planned capacity
Scheduled scaling, GPU reservations and batch jobs.
Engines you choose
plow, vLLM and SGLang side by side.

Faster first words on the same GPUs

plow plans each model before it serves, so replies start sooner.

First token, typical

plow 36.6 ms
vLLM 69.7 ms

First token, slowest 1%

plow 251 ms
vLLM 369.5 ms

Each next token, typical

plow 5.09 ms
vLLM 10.48 ms
Gemma-4 26B FP8, one H100, production-style chat at equal throughput. Settings and raw data

  1. Fifty voice calls on one H100

    Speech-to-text, the LLM and text-to-speech share one GPU.

  2. Planned ahead of time

    Each model compiles for its GPU, so start-up skips the warm-up.

  3. Checked before it ships

    Our own kernels, cuBLAS where it’s still faster, and an accuracy check on every result.

Built for teams serving their own models

  • Voice AI teams

    Speech-to-text, the LLM and the voice on one GPU, with replies that start sooner.

  • Companies with private GPUs

    Your models behind your firewall, with keys, limits and usage per team.

  • GPU clouds

    Offer managed inference to your tenants on NVIDIA and AMD.

  • Teams without GPUs

    Inference on dedicated GPUs we bring in, sized to your workloads and commitment.

Results, with every setting shown

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Bring one model and one GPU

A 30-minute session with an engineer, on your models and hardware.

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