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#cpu-offload

ExLlamaV3 is underrated

Reddit r/LocalLLaMA ↗ · 2026-09-07

The author advocates for ExLlamaV3, an underrated local AI inference tool that outperforms llama.cpp on NVIDIA hardware, with recent updates like CPU MoE offload enhancing its performance.

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#cpu-offload

ExLlamav3 Recent Updates : CPU offload, GLM-5.3-FLASH, Qwen3.8-Flash, SC Quants ++

Reddit r/LocalLLaMA ↗ · 2026-09-01

ExLlamav3 has released major updates including CPU offload for MoE experts, support for new AI models like GLM-5.3-Flash and Qwen-3.8-Flash, and various performance optimizations.

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#cpu-offload

Auto-fit vs tuned MoE offload: 564 → 1330 pp tok/s, unchanged decode (Qwen3.6-35B-A3B Q6 / RTX 3090)

Reddit r/LocalLLaMA ↗ · 2026-08-06

A developer benchmarks Qwen3.6-35B-A3B Q6 on an RTX 3090, showing that offloading eight MoE expert layers to CPU and increasing batch sizes improves prompt processing by 2.36× (564→1330 tok/s) with no decode speed regression, using evolutionary search to find the tuning config.

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#cpu-offload

Running Qwen3.6-35B-A3B on a laptop RTX 4060 (8GB) — what worked, what didn't, and a surprising speculative-decoding result

Reddit r/LocalLLaMA ↗ · 2026-06-05

A detailed account of running the Qwen3.6-35B-A3B MoE model on an 8GB laptop GPU, covering effective optimizations like --no-mmap and VRAM headroom, unexpected findings where speculative decoding improved speed by 26% contrary to benchmarks, and pitfalls with Windows and CPU bottlenecks.

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