A developer released PicoLM v1.0-rc1, an LLM inference engine written in C99, on GitHub and announced it on Hacker News. It supports llama-2, GPT-2, Qwen 3.6/3.8 with MoE, and Gemma-3n models, with heavy emphasis on CPU SIMD acceleration and cross-platform portability. For practitioners, it offers a lightweight inference option spanning platforms from DOS to modern OSes.
A developer released Project Zero, an inference engine written from scratch in pure C99 that runs both 1.58-bit ternary BitNet models and standard dense GGUF models as a single binary with no Python or CUDA. On a Xeon it reports 36 tok/s on BitNet b1.58-2B-4T, about 1.8x faster than bitnet.cpp. It ships with an OpenAI-compatible API and offers a pre-built Linux binary, though some quantization paths still lag llama.cpp.
A developer released Project Zero, an inference engine written from scratch in pure C99 that runs both 1.58-bit ternary BitNet models and standard dense GGUF models as a single binary with no Python or CUDA. On a Xeon it reports 36 tok/s on BitNet b1.58-2B-4T, about 1.8x faster than bitnet.cpp. It ships with an OpenAI-compatible API and offers a pre-built Linux binary, though some quantization paths still lag llama.cpp.
The developers of Paddock, a Rust/C++ inference engine with its own CUDA kernels, released the code as open source on schedule, fulfilling an August commitment to ship in September. The public repo is their internal repo, including kernels, and the engine already serves roughly 300B tokens per year in production at their company. Practitioners get a single binary exposing OpenAI- and Anthropic-style APIs that loads GGUF and safetensors weights.
The developers of Paddock, a Rust/C++ inference engine with its own CUDA kernels, released the code as open source on schedule, fulfilling an August commitment to ship in September. The public repo is their internal repo, including kernels, and the engine already serves roughly 300B tokens per year in production at their company. Practitioners get a single binary exposing OpenAI- and Anthropic-style APIs that loads GGUF and safetensors weights.
Reame is a new LLM inference server designed to run efficiently on cheap CPU hardware, including shared vCPUs, free tiers, and 2-core ARM boxes. Built on llama.cpp, it features disk KV cache, self-regulating speculation, generation archive, and interleaved multi-user support. The project emphasizes treating CPU hardware as a first-class citizen rather than a fallback.
Reame is a new LLM inference server designed to run efficiently on cheap CPU hardware, including shared vCPUs, free tiers, and 2-core ARM boxes. Built on llama.cpp, it features disk KV cache, self-regulating speculation, generation archive, and interleaved multi-user support. The project emphasizes treating CPU hardware as a first-class citizen rather than a fallback.
gemma4.c is a single-file C program that runs Google's Gemma 4 E2B language model on a CPU, implementing the tokenizer, transformer, KV cache, sampling, and CPU kernels from scratch. It allows developers to understand and run a modern LLM without external inference frameworks, making it educational and lightweight.
gemma4.c is a single-file C program that runs Google's Gemma 4 E2B language model on a CPU, implementing the tokenizer, transformer, KV cache, sampling, and CPU kernels from scratch. It allows developers to understand and run a modern LLM without external inference frameworks, making it educational and lightweight.
A from-scratch LLM inference engine written in Rust and CUDA, specifically tuned for the RTX 5090 Laptop GPU (Blackwell sm_120a). It supports NVFP4 quantization, mixture-of-experts (MoE), and multi-token prediction (MTP) speculative decoding, achieving up to 1.6x speedup over llama.cpp on target models. Designed for developers who need maximum inference performance on a single high-end laptop GPU.
A from-scratch LLM inference engine written in Rust and CUDA, specifically tuned for the RTX 5090 Laptop GPU (Blackwell sm_120a). It supports NVFP4 quantization, mixture-of-experts (MoE), and multi-token prediction (MTP) speculative decoding, achieving up to 1.6x speedup over llama.cpp on target models. Designed for developers who need maximum inference performance on a single high-end laptop GPU.