HN · GitHub Signals

OPEN SOURCE, IN FOCUS

lsglang

guqiong96/lsglang

Lsglang is a special extension of sglang that fully utilizes CPU and GPU computing resources with an efficient GPU parallel + NUMA parallel architecture, suitable for MOE model hybrid inference.

Explore on GitHub ↗

Saved projects stay in this browser. No account needed.

TAKE A CLOSER LOOK

Start with the source.

The repository is the place to check installation steps, supported platforms, the license, and current maintenance before choosing a tool.

Read the documentation on GitHub ↗

Keep exploring

Find projects in the same areas, or search the archive for a different approach.

ANOTHER GOOD FIND

Explore related projects

View the archive ↗

Connected by topics and descriptions.

guqiong96

lvllm

LvLLM is a special NUMA extension of vllm that makes full use of CPU and memory resources, reduces GPU memory requirements, and features an efficient GPU parallel and NUMA parallel architecture, supporting hybrid inference for MOE large models.

Take a look →
dicklesworthstone

franken_ocr

Pure-Rust, CPU-only OCR engine for Baidu Unlimited-OCR (a DeepSeek-OCR-derived 3B MoE VLM). One fixed model, custom int4/int8 kernels, no ML framework, no GPU. Pre-Phase-0 scaffold.

Take a look →
hiyucat

deepseek-v4-pro-flash-desktop-app

deepseek-v4-pro-flash deepseek ai large language model llm mixture of experts moe 1m context window hybrid attention architecture compressed sparse attention csa heavily compressed attention hca manifold constrained hyper deepseek-v4-pro deepseek-v4-flash open source hugging face github repository api access local llm inference vllm ollama

Take a look →
fareedkhan-dev

kimi-k3-in-c

A 2.78-trillion-parameter Kimi K3 running inference on a single CPU in 8.24 GB of RAM. Portable C99: no BLAS, no framework, no GPU.

Take a look →