We run Qwen, DeepSeek, GLM and Kimi on our own GPUs and hand you back tokens the moment they exist. One OpenAI-compatible API. No queue.
You already know how to call an OpenAI-compatible endpoint. Point it at us instead and the same code runs against open weights, on hardware we own.
Prefix caching keeps the shared start of your conversations resident on the GPU. Repeat calls skip straight past it — and we bill those cached tokens at a fraction of the normal rate.
When a GPU is full we return a 429 immediately instead of
holding your request. Queueing looks like speed on our dashboard and
feels like slowness in your app. We'd rather refuse than pretend.
Every token is flushed to your socket the moment it exists — no batching, no compression, no proxy sitting on the stream. First token out is first token seen.
Open weights, run on our own metal. Every model keeps the licence its authors published it under.
The exact models we serve, their context windows, capabilities and current
pricing are always at
api.llmfa.st/v1/models.
That endpoint is the source of truth; nothing on this page is.
You can also compare us against every other provider on
OpenRouter.
Change two lines. If your code already talks to an OpenAI-compatible API, it already talks to us.
# Python — the OpenAI SDK, pointed at us from openai import OpenAI client = OpenAI( base_url="https://api.llmfa.st/v1", api_key="sk-llmfast-...", ) stream = client.chat.completions.create( model="qwen/qwen3-32b", messages=[{"role": "user", "content": "Hello"}], stream=True, ) for chunk in stream: print(chunk.choices[0].delta.content or "", end="")
Or reach us through OpenRouter, where we sit alongside every other provider and you can compare us on price and speed before sending a single request.
Questions? Just email us — a person reads it.