LLM local
Web LLM
High-performance In-browser LLM Inference Engine
★ 19,2k estrellasApache-2.0
Ver la guía →TokenSpeed es un proyecto de LLM local de código abierto de lightseekorg (lightseekorg/tokenspeed en GitHub). Tiene 2.473 estrellas en GitHub y 336 bifurcaciones, se publica bajo la licencia MIT y se actualizó por última vez en GitHub el 15 de octubre de 2026.
TokenSpeed — a speed-of-light LLM inference engine designed for agentic workloads, with TensorRT-LLM-level performance and vLLM-level usability
TokenSpeed’s docs cover installation and requirements for your GPU stack.
Follow the launching guide to serve a model with the engine.
Use the server parameters, model recipes and parallelism guides to fit long, tool-heavy agentic sessions.
Scheduling, KV cache ownership and overlap timing are modelled as a finite-state machine tuned for agent request patterns.
A pluggable kernel registry includes one of the fastest MLA implementations on NVIDIA Blackwell.
KV resource reuse safety is enforced by the type system at compile time, not by convention.
New agentic-coding models (Qwen3.8 Flash Next, GLM 5.3 Flash) were supported at release.
Start from the source and the linked guides.
git clone https://github.com/lightseekorg/tokenspeed.gitInstallation and first run, per the official docs.
https://lightseek.org/tokenspeed/guides/getting-startedThe launching guide covers serving configuration.
https://lightseek.org/tokenspeed/guides/launchingThe TokenSpeed Team at LightSeek (lightseekorg).
Yes, MIT-licensed and open source.
No — it is a self-hosted inference engine; you bring the hardware and the model weights.
vLLM is the general-purpose usability benchmark and TensorRT-LLM the performance one. TokenSpeed targets both at once, with an architecture specialised for agentic workloads rather than chat traffic.
Clone the repo and follow the official getting-started and launching guides at lightseek.org/tokenspeed.
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