Introducing the next evolution in LLM text generation: the first fully-fledged…
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Introducing the next evolution in LLM text generation: the first fully-fledged serving system built around a decode megakernel. Delivering up to 1.58x faster performance than vLLM. Built for North Mini Code, completely open-source.
Context
When a generates text, the decoding step, producing one after another, is usually handled by a sequence of separate GPU operations. Cohere's new serving system instead fuses those operations into one megakernel, which it says removes overhead between steps and delivers up to 1.58 times the throughput of vLLM, a commonly used open-source model-serving framework, on the same hardware.
It was built for North Mini Code, Cohere's own compact open-source coding model released earlier this year, and the new serving system is itself open-source, so teams running that model or similar small coding models can adopt the same kernel design rather than defaulting to general-purpose serving software.
- LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
- token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
- inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
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