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1. Best Local LLMs You Can Run on a Single 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek Compared

   This guide outlines the most effective approach for running large language models locally on hardware with 24GB of VRAM. It advises moving away from squeezing extremely large parameter models toward using high-performance 20B to 35B class models that allow room for context and fast processing speeds. The article explains how memory is allocated across model weights, KV cache, and runtime overhead while recommending specific top performers:
   
   * Qwen3.6-27B for agentic coding
   * Qwen3.6-35B-A3B MoE for speed in general conversation
   * Gemma 4 26B for multimodal and multilingual support
   * Mistral Small 3.2 24B as a low-latency assistant
   * gpt-oss-20b for structured reasoning tasks
   * DeepSeek-R1-Distill-Qwen-32B for deep logical reasoning through chain of thought

   2026-07-22 Tags: local llm, llm, gpu, inference, 24gb vram, quantization, qwen, gemma 4, mistral small, deepseek r1, mixture of experts by klotz

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