---
title: "🧬 BitNet b1.58 2B4T — CPU Inference (bitnet.cpp)"
emoji: ⚡
colorFrom: indigo
colorTo: blue
sdk: docker
pinned: false
models:
  - microsoft/bitnet-b1.58-2B-4T
  - microsoft/bitnet-b1.58-2B-4T-gguf
tags:
  - bitnet
  - 1-bit
  - cpu-inference
  - ternary-weights
  - bitnet-cpp
  - llama-server
short_description: "1-bit LLM on CPU with bitnet.cpp — 4-10x faster"
---

# ⚡ BitNet b1.58 2B4T — CPU Inference with bitnet.cpp

The **fast** version. This Space compiles Microsoft's [bitnet.cpp](https://github.com/microsoft/BitNet) from source and runs the official GGUF model with the **I2_S lossless kernel** — achieving **4-10× speedup** over the transformers-based version.

## Architecture

```
┌─────────────────────────────────────────────────┐
│  Gradio UI (Python)                             │
│    ↕ OpenAI-compatible streaming API            │
│  llama-server (bitnet.cpp, port 8080)           │
│    ↕ I2_S ternary kernel (additions only)       │
│  ggml-model-i2_s.gguf (1.1 GB, lossless)       │
└─────────────────────────────────────────────────┘
```

## Why bitnet.cpp?

| Engine | Speed | Lossless? | Notes |
|---|---|---|---|
| **bitnet.cpp I2_S** | ~10-15 tok/s | ✅ 100% | Optimized ternary kernel |
| transformers (bf16) | ~1.4 tok/s | ✅ | No ternary optimization |
| llama.cpp TQ1_0 | ~2 tok/s | ❌ 1.4% | Quality degraded |

## Features

- 💬 **Streaming Chat** — Real-time conversation with live tok/s stats
- 📊 **Benchmark** — Greedy generation with detailed performance metrics
- 📈 **Paper Results** — Published comparison table from the technical report
- 🏗️ **Architecture** — How ternary weights enable multiplication-free inference
- ⚙️ **System Info** — Live CPU/memory stats

## How it works

1. **Docker build** compiles bitnet.cpp with I2_S kernel optimizations
2. **Container startup** launches `llama-server` on localhost:8080
3. **Gradio app** connects via OpenAI-compatible streaming API
4. **All inference** happens on CPU with addition-only matrix operations

## References

- 📄 [Technical Report](https://arxiv.org/abs/2504.12285)
- 📄 [bitnet.cpp Paper](https://arxiv.org/abs/2502.11880)
- 🤗 [Model Weights](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T)
- 🤗 [GGUF Weights](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T-gguf)
- 💻 [bitnet.cpp](https://github.com/microsoft/BitNet) (38K+ ⭐)