User reports Qwen3.6 35B-A3B model improves with Q8_0 CPU quantization
A user on Reddit reported that switching from Q4_K_M on GPU to Q8_0 on CPU significantly improved the performance of the Qwen3.6 35B-A3B model for a complex coding task. The user noted the model 'punches far above its weight' and found the quality gain worth the slowdown.
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User runs 100B+ MoE LLMs on low-end laptop using NVMe swap and Q3 quantization
A Reddit user with a low-spec laptop (i7-8750H, 20GB RAM, GTX 1050 4GB) reports successfully running 100B+ parameter MoE models by offloading parameters to a Samsung NVMe SSD via mmap, using Q3 quantization and quantized KV cache (Q4_0). They note that dense models are unusable on their hardware, but MoE models work with experts offloaded to CPU.
Voodoo Quant claims 95% KLD improvement over Unsloth Dynamic 2.0 on Qwen3.5 models
A developer released two new GGUF quantizations of Qwen3.5 0.8B and 2B using a technique called Voodoo Quant, which optimizes mixed precision by assigning higher precision to more important parts of the model. The author claims Voodoo Quant beats Unsloth Dynamic 2.0 by 95% in Kullback-Leibler divergence (KLD). The quantized models are available on Hugging Face.
Turboquant v0.3.0 fixes silent FP16 precision bug on Tesla P100 in llama.cpp
A three-line fix in turboquant v0.3.0 corrects a long-standing bug where llama.cpp's CUDA code forced FP16 math on Tesla P100 GPUs, despite the P100 having fast FP16 hardware. The fix restores correct precision and performance for P100 users running llama.cpp.
↑ Updated Sun, Jul 12, 2026, 05:41 AM — Turboquant v0.3.0 released with fix for P100 FP16 bug.
User seeks advice on adding second cheap GPU to run larger local models
A Reddit user running Gemma 4 26B-A4B at 12-15 t/s on an RTX 3060 (12 GB) finds the model insufficiently intelligent and wants to upgrade to a 31B model, which runs at only 1.5 t/s. They ask the community about the benefits of adding a second cheap GPU to improve performance for local LLM inference.
User benchmarks AMD EPYC 9374F for LLM inference, finds 48-thread sweet spot
A user replaced their EPYC 9135 with a cheap 9374F (8 CCDs) for LLM inference. Initial benchmarks showed no decoding advantage until they used 48 threads; 64 or 32 threads performed worse than the 9135 in some scenarios. The 9374F is worse for gaming.