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A single-binary LLM inference engine built from scratch, implemented in C99, that efficiently runs Microsoft's BitNet b1.58-2B-4T model on consumer CPUs—no GPU, no Python, no framework dependencies required.
Section 01
A single-binary LLM inference engine built from scratch, implemented in C99, that efficiently runs Microsoft's BitNet b1.58-2B-4T model on consumer CPUs—no GPU, no Python, no framework dependencies required.
Section 02
Section 03
Project Zero is a single-binary LLM inference engine built from scratch, fully written in C99. Its core goal is to efficiently run Microsoft's BitNet b1.58-2B-4T model on consumer CPUs—no GPU, no Python, no framework dependencies required. This project represents a significant milestone in edge computing and local AI deployment, proving that pure CPU inference can achieve surprisingly high performance levels.
BitNet b1.58-2B-4T is a 2-billion-parameter large language model with ternary quantized weights (-1, 0, +1). Traditionally, such models require GPUs to achieve acceptable inference speeds, but Project Zero has successfully broken this assumption through extreme CPU optimizations.
Section 04
Project Zero chooses C as its implementation base, bringing several key advantages:
Section 05
The core of BitNet lies in its ternary weights (each weight is either -1, 0, or +1). Project Zero implements a 16-wide AVX-512 packed kernel, achieving twice the throughput compared to AVX2. Weights are packed at a density of 4 values per byte, significantly reducing memory bandwidth requirements.
Section 06
The engine uses a sliding-window KV cache with int8 quantization support, capable of handling a 131K context length with reasonable memory usage. This is crucial for long-document analysis and conversational applications.
Section 07
On Intel Xeon @ 2.10 GHz (Emerald Rapids architecture, 4 cores, 260MB L3 cache):
| Configuration | Speed | Notes |
|---|---|---|
| Baseline (AVX-512F Floating-Point FMA) | 16.47 tok/s | Ternary floating-point path |
| + INT8 VNNI Classifier | 21.20 tok/s | 28.7% improvement |
| + VBMI3 Instruction Unpacking | 32.65 tok/s | 2.7x faster ternary layers |
| + INT4 Classifier + PGO/LTO | 36.25 tok/s | Reaches 95% of DRAM bandwidth limit |
Section 08
| Engine | Average Speed | Best Speed |
|---|---|---|
| Project Zero | 34.75 tok/s | 36.25 tok/s |
| bitnet.cpp | 19.33 tok/s | 19.83 tok/s |
| Advantage | 1.80x | 1.83x |
This means that on the same hardware, Project Zero's throughput is almost twice that of the official bitnet.cpp.
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