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Users are reporting critical issues with Mixture of Experts (MoE) model performance including significant decode throughput regressions, quantization-related accuracy problems with new models like Gemma 4 and Qwen3, and CUDA/ROCm backend stability issues causing crashes and hangs. These fixes are essential for running large-scale MoE deployments reliably and efficiently.

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已分析 Issue35

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需求簇1

更新时间2026-04-06

头号需求

MoE Performance, Quantization, and Backend Stability Fixes

4.5 得分

上升需求

MoE Performance, Quantization, and Backend Stability Fixes

36.0x

主导分类

Performance

LLM Inference Engine

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当前最重要需求

1

MoE Performance, Quantization, and Backend Stability Fixes
Performance

Users are reporting critical issues with Mixture of Experts (MoE) model performance including significant decode throughput regressions, quantization-related accuracy problems with new models like Gemma 4 and Qwen3, and CUDA/ROCm backend stability issues causing crashes and hangs. These fixes are essential for running large-scale MoE deployments reliably and efficiently.

35 条 issue 4.5 得分