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.
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.
Optimized ESP32-S3/P4 fully offline speech to text and text to speech system for Micropython and AtomVM, featuring a finetuned STT model for noisy environments, plus a data generation/field capture/model finetuning framework. Includes high performance 4/8bit int kernels for Xtensa/RiscV PIE vector units
Run the native 284B-A13B DeepSeek-V4-Flash-0731 LLM locally on one laptop CPU: pure C, 8 GB RAM minimum, no GPU, best TPOT 0.892 s/token, resident OpenAI-compatible API with function tools. | 在笔记本单颗 CPU 上本地运行原生 284B-A13B DeepSeek-V4-Flash-0731 大模型:纯 C,最低 8 GB 内存,无需 GPU,最优 TPOT 0.892 秒/token,支持模型常驻的 OpenAI 兼容接口与函数工具。
Pure C implementation of FastEnhancer. 3.3x faster than the ONNX Runtime graph, same quality.