sergqwer / strata-nvfp4
Strata fork: Qwen3.8-Flash-Next 125B MoE in NVFP4 on one RTX 20-50 card (12 GB+) and 64 GB of RAM or more. Setup installs our GPTQ quants (huihui abliterated, OrcaRouter) from Hugging Face. CPU experts on AVX-512 or AVX2 (+AVX-VNNI), all experts in RAM from 92 GB, a low-RAM mode below, vision, W4A8 prompts. Windows / CUDA 13.
intel / auto-round
PTQ-Level Efficiency, QAT-Level Accuracy | 训练后量化的效率，量化感知训练的精度
ModelCloud / GPTQModel
LLM model quantization (compression) toolkit with HW acceleration support for Nvidia, AMD, Intel GPU and Intel/AMD/Apple CPU via HF, vLLM, and SGLang.
TechNomadCode / AI-Product-Development-Toolkit
Plan, build and launch products with AI: guided planning prompts, an AI App Starter for Next.js, Supabase and Vercel, and agent configurations.
intel / neural-compressor
SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime
Xilinx / brevitas
Brevitas: neural network quantization in PyTorch
MikeCaldera / intel-arc-pro-b70-qwen38-vllm
Reproducible Qwen3.8-27B GPTQ INT4 + MTP4 inference on Intel Arc Pro B70 using vLLM XPU — 84.65 tok/s median.
WestWaters / pollard-weights
A tiered-memory system design for workloads that don't fit in RAM: measure the working set, pin the hot tier, stream the cold tier from flash. Ships the residency calculator, measurement harnesses, and the build recipes behind it. Predictions validated against public benchmarks.
paperniuk / lmstudio-prism-bonsai
Make LM Studio load Ternary Bonsai 2 GGUF (PQ2_0 / PTQ1_0) via Prism's llama.cpp fork, with reasoning-effort selector. Linux + Windows, double-click install.
RadixRootMind / Awesome-Quant-VLA-WAM
Curated quantization research for VLA & WAM models — 180+ papers, a five-category taxonomy, a roadmap, and a companion PTQ + LIBERO toolkit (quant-vla).