Getting Started Articles

Step-by-step BitNet installation guide for Windows, macOS, and Linux — optimized for CPU inference, edge deployment, and 1-bit LLMs.

BitNet uses true 1-bit weights and activations — enabling fast, lightweight CPU inference impossible with traditional LLMs. See benchmarks, code, and deployment tips.

A practical, engineer-led walkthrough of the BitNet GitHub repository — mapping each directory to 1-bit LLM development, CPU inference, and edge deployment.

BitNet runs 1-bit LLMs on CPUs with AVX2/NEON and 4–16 GB RAM — no GPU needed. This guide details exact specs, benchmarks, and real-world deployment tips.

New to BitNet? Avoid these 10 critical mistakes — from unpacked weights to activation mismatches — and ship performant 1-bit LLMs on CPU.

Set up a production-ready BitNet development environment optimized for CPU inference, 1-bit LLMs, and edge deployment — no GPU required.

A practical, developer-first glossary of essential BitNet terms — from BitLinear and b1.58 variants to CPU inference optimizations and edge deployment constraints.

Learn what BitNet is, how 1.58-bit quantization works, and how to get started running large language models on your CPU without a GPU. A complete beginner's guide to 1-bit LLMs.