Best Local LLMs for a Single 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek

A single 24GB GPU (e.g., RTX 3090/4090) is the practical threshold for local inference. Memory consumption comes from model weights, KV cache, and runtime overhead. With Q4_K_M quantization, each parameter takes ~0.58 bytes, so a 32B model alone needs 18-20GB for weights. MoE models count total parameters, not active per token. The six recommended models all use permissive licenses (Apache 2.0 or MIT): Qwen3.6-27B (~16GB) excels at coding and repository-level reasoning; Gemma 4 26B MoE (~20GB) supports multimodal and 140+ languages; Mistral Small 3.2 24B (~14GB) suits daily assistant tasks with longer context; DeepSeek-R1-Distill-Qwen-32B (~18-20GB) offers strongest reasoning but tightest fit. For developers running local models, the most practical advice is: don't try to fit the largest 70B quantized model; instead choose a 20-35B model of appropriate size. Recommended quantization level is Q4_K_M as a balance; Q8_0 or BF16 usually can't fit models above 30B. For running tools, Ollama is simplest, llama.cpp offers fine control, and vLLM suits high concurrency. Also note server-grade MoE models like GLM-5.2, Kimi K2.7, DeepSeek V4 cannot run on a single 24GB GPU; consider API solutions.