Best GPU for Local LLM Models

A 27B dense vision-language model whose hybrid linear attention keeps the KV cache tiny — it runs a 128k+ context on a single 16-24 GB card, with a native 262k window extensible to 1M.

A 284B Mixture-of-Experts model with just 13B active params and a 1M-token context. Weights are large enough to need multi-GPU or CPU-offload, but only 13B activate per token so decode stays fast.

OpenAI's 117B open-weight Mixture-of-Experts model with 5.1B active params. Its native MXFP4 weights fit a single 80-96 GB GPU, and the tiny active count keeps decode fast.

State-of-the-art dense model with deep math, coding, and engineering comprehension.

Massive Mixture-of-Experts coder optimized for 16GB–24GB VRAM target setups.

Google's premier high-density MoE architected for complex coding and reasoning.

Lightweight, high-speed dense model optimized for standard consumer configurations.

Top-tier reasoning distilled into a compact 14B model. Strong STEM and coding performance on any 12GB+ GPU.

Mid-size reasoning powerhouse for 24GB cards. Matches frontier models on math, coding, and logic benchmarks.

Efficient dense model with broad multilingual support and native function calling. Fits 16GB cards at Q4.

Meta's most accessible MoE flagship — 17B active parameters deliver strong quality on workstation hardware.

Versatile 7B dense model built for efficient local inference on entry-level to mid-range GPUs.

OpenAI's consumer reasoning model, built to run uncompromised on mainstream local hardware.

Ultra-compact 4B NVIDIA model that fits comfortably on nearly any modern GPU — ideal for edge and portable setups.

Huge 80B Mixture-of-Experts with only 3B active parameters — needs a 48GB workstation card, but generates as fast as a small model.