About Best GPU for LLM

We help local LLM users find the right GPU for the models they actually want to run — Ollama, llama.cpp, vLLM, and everything else that keeps inference on your own hardware.

Our mission

Local LLM hardware advice is surprisingly hard to find. Most GPU reviews focus on gaming frame rates. Most AI buyer guides gloss over the quantization and VRAM detail that actually determine whether a model will run. We built Best GPU for LLM because the right answer for most readers — often a used RTX 3090 or a budget 16GB card — rarely shows up in generic reviews.

The editorial team

Best GPU for LLM is written and maintained by a small editorial team focused on local LLM hardware. Our background spans Ollama and llama.cpp deployment, quantization tradeoffs, multi-GPU setups for large models, and privacy-focused local inference workflows. We track new model releases (Llama 4, Qwen 3, Gemma 3, DeepSeek, Phi-4) and update hardware guidance as the landscape moves.

We are deliberately organized as an editorial brand rather than a personality-led site. The focus is on the recommendations, methodology, and ongoing updates.

What we cover

• Buyer guides — "Best GPU for [use case]" articles covering budget tiers from $250 used to $2,000 flagship, always framed around the models you can actually run.
• Model-specific guides — Llama 3, Llama 4 (Scout, Maverick), Qwen 2.5 and Qwen 3, Mistral, Gemma, DeepSeek, Phi-3 and Phi-4.
• VRAM and quantization guides — exact VRAM numbers for 7B, 13B, 34B, 70B models at every Q-level with realistic context overhead.
• Platform and tool comparisons — NVIDIA vs AMD, Mac vs discrete, Ollama vs llama.cpp vs vLLM, cloud vs local.

How we work

Every article follows a structured evaluation that prioritizes VRAM fit, memory bandwidth, KV cache overhead, and price-to-value across new and used markets. Full details on our Methodology page and Editorial Policy.

Who this is for

Anyone running LLMs locally — developers replacing cloud APIs with self-hosted inference, researchers doing iterative work, privacy-focused users who want models on their own hardware, and hobbyists exploring open-weight models without monthly subscription costs.

Contact and feedback

Spotted an outdated benchmark, a tok/s number that does not match your setup, or a recommendation that misses your use case? Reach out through the channels on our contact page and we will update the article.

Need advice beyond local LLMs?

We keep this site on local inference. For image and video generation, training rigs and general AI workstations, Best GPU for AI covers ground we deliberately leave out.