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bitnet.c is a zero-dependency, pure C11-written large language model (LLM) inference engine that supports CPU-side NEON/AVX2 SIMD acceleration, Flash MoE expert caching, TurboQuant 3-bit KV compression, and other technologies, enabling efficient operation on resource-constrained devices.
Section 01
bitnet.c is a zero-dependency, pure C11-written LLM inference engine designed specifically for resource-constrained devices. It supports CPU-side NEON/AVX2 SIMD acceleration, Flash MoE expert caching, TurboQuant 3-bit KV compression, and other technologies, enabling efficient operation and wide application scenarios.
Section 02
bitnet.c takes minimalism as its core philosophy, removing all external dependencies and relying only on the standard C11 library. This design brings three major advantages: portability (supports almost all C compiler platforms), auditability (simplified code for easy security review), and deployment convenience (runs as a single binary file with no dependency conflicts).
Section 03
bitnet.c uses NEON (ARM) and AVX2 (x86) instruction sets to implement SIMD acceleration, enabling considerable performance even without a GPU, making it suitable for edge/embedded environments.
For MoE models, it combines prefetching and LRU caching strategies to reduce memory access latency during expert switching, ensuring fast access to active expert data.
Section 04
Compresses KV cache to 3-bit precision, achieving an 8.9x memory saving while maintaining acceptable output quality.
Supports over 20 GGUF quantization formats (1-bit to 8-bit), allowing users to flexibly balance performance and quality.
Can be compiled into WASM, embedded into web applications, and run LLMs directly in the browser without installing software.
Section 05
bitnet.c is suitable for the following scenarios:
Section 06
bitnet.c uses a single-file design (amalgamated build) for easy integration; a custom memory pool allocator reduces system memory calls; quantization computation is optimized with lookup tables, converting floating-point operations to integer table lookup operations to improve efficiency.
Section 07
bitnet.c is an important attempt at lightweight LLM inference, proving that high-performance inference does not require complex software stacks or expensive hardware. In the future, with technological progress, more daily devices will be able to run powerful LLMs. For developers, it is a practical tool and learning case, providing reference for similar projects.
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