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Users discuss Q1/Q2 quantization of large language models on r/LocalLLaMA

A discussion on the r/LocalLLaMA subreddit explores the usability of heavily quantized large language models, specifically those in the Q1 or Q2 range for models between 100-250 billion parameters. Users are sharing their experiences with these lower-quantization models for tasks like agentic coding, writing, and chatting, and reporting any issues encountered such as looping or repetition. The thread also lists several recent large models, including DeepSeek-V4-Flash, Qwen3-235B-A22B, and NVIDIA-Nemotron-3-Super-120B-A12B, to provide context for the discussion. AI

IMPACT Provides insights into the practical performance and limitations of running large language models with aggressive quantization on consumer hardware.

RANK_REASON Discussion on a subreddit about the practical use of quantized models.

Read on r/LocalLLaMA →

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

Users discuss Q1/Q2 quantization of large language models on r/LocalLLaMA

COVERAGE [1]

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

    How many of you do use Q1 or Q2 of Big models(100-250B)? How's it?

    <!-- SC_OFF --><div class="md"><p>Sharing popular(also recent) models for reference:</p> <p><strong>151-250B</strong> :</p> <ul> <li>DeepSeek-V4-Flash</li> <li>Step-3.X-Flash</li> <li>Command-a-plus-05-2026</li> <li>Laguna-M.1</li> <li>MiniMax-M2.X</li> <li>Qwen3-235B-A22B</li> <…