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.
- avx2
- c99
- cpu-inference
- deep-learning
Repository Index
77,852 visible repositories in the index
1–7 of 7 · Most stars
Locally run an Instruction-Tuned Chat-Style LLM
Software in C and data files for the popular GloVe model for distributed word representations, a.k.a. word vectors or embeddings
Chinese-Vicuna: A Chinese Instruction-following LLaMA-based Model —— 一个中文低资源的llama+lora方案，结构参考alpaca
- llama
- alpaca
- chinese
- vicuna
Run a 1-billion parameter LLM on a $10 board with 256MB RAM
Unified Communication X (mailing list - https://elist.ornl.gov/mailman/listinfo/ucx-group)
- rdma
- hpc
- networking
- infiniband
37 tok/s from Qwen3-Next-80B (48 GB) on a 32 GB laptop, 24 without drafts vs 2.8 for llama.cpp; reading 62 vs 16 tok/s. Faster than llama.cpp on all 13 models compared in Q4_K_M. LLMs bigger than your RAM, pure C, CPU or GPU.
- avx2
- c
- cpu-inference
- gguf