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

Quantization is how 70B models fit on one GPU. These 9 GitHub projects are the ones doing it.

## AI Quantization Techniques on GitHub 2026 Quantization reduces model precision (FP16 to INT4) to cut memory and speed up inference. Star counts below are from the GitHub API snapshot on 2026-07-31. ### The essential projects 1. **ggml-org/llama.cpp** - 122,228 stars. GGUF format, the de-facto standard for running quantized LLMs on CPU/GPU. 2. **unslothai/unsloth** - 69,279 stars. 2x faster fine-tuning with dynamic quantization; also exports GGUF/QLoRA. 3. **vllm-project/vllm** - 87,794 stars. Production serving with AWQ/GPTQ/FP8 quantization support. 4. **IST-DASLab/gptq** - the original GPTQ repo, ancestor of most weight-only quantization. 5. **mit-han-lab/llm-awq** - activation-aware weight quantization, popular for edge deployment. 6. **QualcommAI/aimet** - model compression toolkit for on-device AI (PyTorch/TensorFlow). 7. **microsoft/DeepSpeed** - includes quantization-aware training and ZeRO-Infinity offloading. 8. **huggingface/optimum** - quantization integrations (GPTQ, AWQ, bitsandbytes) behind one API. 9. **bitsandbytes** - 8-bit/4-bit quantization for training, used by most fine-tuning stacks. ### Which technique when - **GGUF (llama.cpp)**: local inference on consumer hardware - **GPTQ/AWQ**: serving with vLLM, minimal accuracy loss - **QLoRA (bitsandbytes)**: fine-tune on limited GPU memory - **FP8**: the 2026 default for datacenter GPUs (H100/B200) ### Real-world example A Llama-3.1-70B model is about 140 GB in FP16. At INT4 (4-bit), it drops to roughly 35 GB - small enough for a single RTX 4090 (24 GB with offloading) or an A100. ### 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.