BitNet b1.58 — Local AI Model by Microsoft

作者： Jakub Rusinowski · 最后更新： 2024年3月1日

微软带来的研究范式革命。BitNet b1.58用三元{-1, 0, 1}权重替代16位权重，将矩阵乘法（MatMul）转变为简单加法，在CPU上实现极速推理与超高效率。

Licence

Licence	What it permits	Applies to
`MIT`	Commercial use permitted Commercial use permitted. No usage restrictions beyond attribution.	BitNet b1.58 3B

Hardware Requirements

BitNet b1.58 3B Min 3 GB VRAM · 1.58-bit · 2,048 ctx · ollama run hf.co/1bitLLM/bitnet_b1_58-3B

Recommended GPU

The cheapest GPU that runs BitNet b1.58 locally (min 3 GB VRAM) is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB

10 GB VRAM · 150 W board power

2026年价格波动较大——请以当前商品页价格为准。

在亚马逊查看价格

How to Run Locally

Install Ollama then run: ollama run hf.co/1bitLLM/bitnet_b1_58-3B

Minimum VRAM: 3 GB. For best results use Q4_K_M quantization.

BitNet b1.58 — Frequently Asked Questions

How much VRAM does BitNet b1.58 need?

BitNet b1.58 needs about 3 GB VRAM at 1.58-bit quantization for its smallest variant. Variants: BitNet b1.58 3B (3 GB, 1.58-bit). On Apple Silicon, unified memory counts toward this requirement.

Can I run BitNet b1.58 on an RTX 4090 (24 GB)?

Yes — BitNet b1.58 runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at 1.58-bit.

What quantization should I use for BitNet b1.58?

Q4_K_M is the best balance of quality and VRAM for BitNet b1.58 in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.

How do I run BitNet b1.58 with Ollama?

Install Ollama, then run: ollama run hf.co/1bitLLM/bitnet_b1_58-3B. This downloads BitNet b1.58 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.