BitNet b1.58 — Local AI Model by Microsoft

Written by Jakub Rusinowski · Last updated July 10, 2026

A research paradigm shift by Microsoft. BitNet b1.58 replaces 16-bit weights with ternary {-1, 0, 1} weights. This eliminates Matrix Multiplication (MatMul) in favor of simple addition, offering extreme speed and efficiency on CPUs.

Hardware Requirements

BitNet b1.58 3BMin 2 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 2 GB VRAM) is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB
Launch MSRP: $219
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How to Run Locally

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

Minimum VRAM: 2 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 2 GB VRAM at 1.58-bit quantization for its smallest variant. Variants: BitNet b1.58 3B (2 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.