SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime
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Updated
Sep 4, 2026 - Python
SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime
[EMNLP 2024 & AAAI 2026] A powerful toolkit for compressing large models including LLMs, VLMs, and video generative models.
Qwen3-8B quantization study across vLLM, TensorRT-LLM, AutoRound, INT8, and MXFP4
Compress context data to optimize memory and performance in C++ large language model applications within the llm-cpp toolkit.
GPTQ / AWQ / SmoothQuant 从零实现:同模型、同评测协议、同量化网格下的控制变量对照 —— GPTQ 收回 RTN 损失 61.6%、AWQ 31.9%、叠加仅再 +1.0pp
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