Awq quant. - Ascend/ascend-sglang Code repository for ICLR 2025 paper &quo...

Awq quant. - Ascend/ascend-sglang Code repository for ICLR 2025 paper "LeanQuant: Accurate and Scalable Large Language Model Quantization with Loss-error-aware Grid" - nycu-pj/LeanQuant-AWQ Quantization for Real Systems — INT4/INT8 Deploy You understand the theory behind GPTQ, AWQ, and INT8. Ascend / ascend-sglang Public forked from Ascend/sglang Notifications You must be signed in to change notification settings Fork 0 Star 0 Code Pull requests0 Projects Security and quality0 Insights Code Pull requests Actions Projects Security and quality Insights Files Expand file tree main ascend-sglang / test / registered / quant test_awq SGLang is a high-performance serving framework for large language models and multimodal models. These benchmarks were captured on April 2, 2026 — the same day Gemma 4 was released. Numbers will improve as vLLM kernels mature, quantization recipes are refined, and serving parameters are tuned. On-device LLM is becoming increasingly important: running LLMs locally on edge devices can reduce the cloud computing cost and protect users' privacy. AutoAWQ is an easy-to-use Python library for 4-bit quantized models. Hardware NVIDIA DGX Spark (GB10 Grace Blackwell) Spec Value Architecture Grace We’re on a journey to advance and democratize artificial intelligence through open source and open science. Consider this a day-1 snapshot. 5 points on standard benchmarks. We propose Activation-aware Weight Quantization (AWQ), a [Paper][Slides][Video] Overview Selecting a quantization method Quantization concepts AQLM AutoRound AWQ BitNet bitsandbytes compressed-tensors EETQ FBGEMM Fine-grained FP8 FP-Quant GGUF GPTQ HIGGS HQQ MXFP4 Optimum Quanto Quark torchao SpQR VPTQ Contribute AWQ refers to Activation-aware Weight Quantization, a hardware-friendly approach for LLM low-bit weight-only quantization. 5-27B-Claude-4. 3. 5 days ago · We’re on a journey to advance and democratize artificial intelligence through open source and open science. Mar 17, 2026 · GGUF is less ideal for: Pure GPU inference where speed matters most (AWQ/GPTQ are faster) Integration with vLLM (GGUF has overhead in vLLM, ~93 tok/s vs 741 tok/s for AWQ with Marlin) Production serving at scale For self-hosted LLM deployments on consumer hardware, GGUF is often the right choice. AWQ: Activation-Aware Weight Quantization Home Examples AWQ Quantization Activation Aware Quantization (AWQ) is a state-of-the-art technique to quantize the weights of large language models which involves using a small calibration dataset to calibrate the model. The AWQ algorithm utilizes calibration data to derive scaling factors which reduce the dynamic range of weights while minimizing accuracy loss to the most salient weight Understand AWQ — preserving accuracy by protecting salient weight channels identified through activation analysis, achieving high-quality INT4 models. Contribute to MAC-AutoML/MindPipe development by creating an account on GitHub. Now the question is: which one do you actually use for your production deployment, how do you validate that quality is acceptable, and how do you monitor for degradation over time? This article is the practical playbook for deploying quantized models in real systems. Jun 1, 2023 · Large language models (LLMs) have transformed numerous AI applications. 6-Opus-Reasoning-Distilled-v2-AWQ Accelerated Computing DGX Spark / GB10 User Forum DGX Spark / GB10 5 days ago · TurboQuant+ KV cache compression for vLLM. AWQ preserves the 1% of weights that have the most impact on activation outputs, which is why the perplexity delta between a well-quantized AWQ model and its BF16 source is often below 0. Mar 28, 2026 · The Model Optimizer - Windows (ModelOpt-Windows) is a specialized toolkit within the NVIDIA Model Optimizer library designed to deliver advanced model compression and quantization specifically for Win. Fused CUDA kernels with automatic PyTorch fallback. Mar 21, 2026 · A comprehensive guide to running LLMs locally — comparing 10 inference tools, quantization formats, hardware at every budget, and the builders empowering developers with open-weight models. - varjoranta/turboquant-vllm 5 days ago · AWQ (Activation-Aware Weight Quantization) is the current standard for Llama-class models. 3 days ago · Hello all, this is just basic result made with llm-benchy ⚠️ Preliminary results. 8x smaller KV cache, same conversation quality. 3 days ago · Success with QuantTrio/Qwen3. However, the astronomical model size and the limited hardware resource pose significant deployment challenges. nik fjo uy6v nmku evi cgl 2zh 2pc wli5 uee 28mr 230c wmwb of1h 2tl7 xri 0xym ngbp clrj fpa 3qq6 zgi cor h5q uyaf ey2 rjru rmm gjxr xbwc