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LLM enthusiasts debate best CPU inference models and software

Users on the r/LocalLLaMA subreddit are discussing the current state of CPU inference for large language models. Participants are seeking advice on optimal models, quantization methods, and specific software versions like llama.cpp for running these models on consumer hardware. One user shared their experience with Qwen3.6 35B on a system with 64GB RAM and AVX2 support, achieving around 10 tokens per second, and is inquiring if better performance is achievable. AI

IMPACT Users are seeking to optimize LLM performance on local hardware, indicating a trend towards decentralized AI deployment.

RANK_REASON User discussion on a subreddit about optimizing LLM performance on consumer hardware.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/ramendik ·

    What's up on CPU inference these days?

    <!-- SC_OFF --><div class="md"><p>What are the best models, quants and llama.cpp versions/forks for CPU inference these days?</p> <p>I have AVX2 but no AVX512 - Intel core ultra 7 165H; 64G RAM</p> <p>This seems to ask for massive MoE (a lot of RAM, not a lot of bandwidth/compute…