Best Local LLMs You Can Run on a Single 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek Compared
MarkTechPost Michal Sutter
A guide compares six open-weight language models optimized for running on a single 24GB GPU, including Qwen3.6-27B, Gemma 4 26B, Mistral Small 3.2 24B, and DeepSeek-R1-Distill-Qwen-32B. These models range from 20B to 35B parameters and use Q4_K_M quantization to fit within memory constraints while leaving room for context and inference overhead. The strategy shifts from squeezing the largest 70B models onto a card to running right-sized 20B–35B dense or efficient mixture-of-experts models that decode faster and leave 1–6GB of headroom for context and serving stack overhead.
Why it matters
A single 24GB GPU is the practical floor for serious local inference. This guide compares six open-weight models that fit one card at Q4_K_M. It covers Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b, and DeepSeek-R1-Distill. Each entry lists VRAM fit, licensing, and the job it does best. The post Best Local LLMs You Can Run on a Single 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek Compared appeared first on MarkTechPost.
