AI Quantization Techniques on GitHub 2026: 9 Projects That Shrink Models

AI quantization on GitHub: 9 projects that shrink LLMs (llama.cpp, unsloth, vLLM, GPTQ, AWQ, bitsandbytes, DeepSpeed, optimum, aimet) with July 2026 stars.

💡 What You Will Learn

AI quantization on GitHub: 9 projects that shrink LLMs (llama.cpp, unsloth, vLLM, GPTQ, AWQ, bitsandbytes, DeepSpeed, optimum, aimet) with July 2026 stars.

AI quantization on GitHub: 9 projects that shrink LLMs (llama.cpp, unsloth, vLLM, GPTQ, AWQ, bitsandbytes, DeepSpeed, optimum, aimet) with July 2026 stars. What quantization is, when to use GGUF vs GPTQ/AWQ vs QLoRA, and how to quantize a model yourself.

❓ FAQ

Does quantization hurt accuracy?

4-bit typically loses 1-3% on benchmarks; 8-bit is nearly lossless.

Can I quantize my own model?

Yes - use llama.cpp convert scripts or huggingface optimum-cli.