Deep Inference
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Models

Open weights. Dedicated endpoints. Retuned weekly.

Every model below runs as your own endpoint, tuned by agents on the silicon where it is cheapest per token today. Throughput is aggregate tok/s per GPU on our stack; pricing is per workload.

Try one live in the playground ↓

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vs stock stack
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tok/s per GPU
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Nothing matches. Any open-weight model can be an endpoint — tell us which one.
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Ask it something. Watch the meter.

A single-stream run against a demo endpoint, same request shape as production. Time to first token, decode speed and the cost of the request are timed live and set against the stock serving stack on the same GPU.

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Request an endpoint →
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Don't see your model? Day-zero support is the job.

Our agents profile every serious open-weight release within hours of the weights dropping. Fine-tunes and custom bases included.

Request a model →bring your weights, we bring the kernels

Throughput is aggregate tok/s per GPU at 4k in / 800 out and 64 concurrent streams on our serving stack, on partner-cloud GPUs. Gain is versus the stock vLLM recipe on the same GPU. Pricing is per workload; start a request. Figures are directional, from internal benchmarks.