Editorial Policy

This page sets out how we make recommendations, handle affiliate relationships, publish corrections, and maintain editorial independence at Best GPU for LLM.

Editorial independence

We do not accept payment, gifts, or preferential access from GPU manufacturers, retailers, or cloud providers in exchange for recommendations. Brands cannot buy a placement or influence a verdict. Every recommendation has to earn its placement on technical merit.

We participate in the Amazon Associates program and cloud GPU referral programs (RunPod, Vast.ai). Commission payout rates never factor into which GPU we recommend.

How we decide what to recommend

• Model-first framing. We start from the model you want to run (Llama 3, Qwen 14B, Mistral 7B) and work toward hardware, not the other way around.
• VRAM is the primary filter. For local LLM inference, VRAM fit determines whether the workload is viable at all. We don't recommend a GPU that cannot comfortably run the target model at a sensible quantization.
• Used-market and cloud honesty. The used RTX 3090 and cloud GPU rentals are compared fairly against new hardware, even when they reduce affiliate upside.
• One clear recommendation. If a reader asks "best GPU for Ollama," we name a card and defend it rather than listing every option.

Sources and citations

We combine manufacturer specs, independent benchmark publications (Tom's Hardware, TechPowerUp, Phoronix), and community LLM-specific data (LocalScore, LM Studio community results, r/LocalLLaMA benchmark threads, Ollama GitHub discussions). Full details on our Methodology page.

Corrections policy

• Factual errors are corrected as soon as we become aware.
• Significant corrections are noted at the top of the article with an "Updated" note.
• Minor typos and clarifications are fixed silently.
• We do not delete articles or rewrite them to hide errors.

If you spot an error, reach out via the channels listed on our About page.

Updates and freshness

LLM hardware guidance evolves quickly — new models, new quantization techniques, shifting pricing. We refresh articles when a new GPU or model launches, prices shift materially, or a reader points out outdated information. We never bump dateModified on unchanged articles to appear fresh.

Content we will not publish

• Articles written to capture search traffic without adding a distinct answer.
• Recommendations driven by affiliate payout rather than technical fit.
• Sponsored posts disguised as editorial (we do not publish sponsored content).
• Fabricated benchmark numbers or expertise claims.

Affiliate relationships

• Amazon Associates — product links to GPUs.
• RunPod referral program — cloud GPU rental.
• Vast.ai referral program — cloud GPU rental.

Full disclosure on the Affiliate Disclosure page.

AI and content generation

We use writing tools — including large language models — to help draft and edit articles, as do most modern editorial teams. Every article is reviewed, fact-checked, and edited against our methodology before publication. We do not publish raw LLM output. Recommendations reflect the editorial team's judgment.

Contact

Editorial feedback, corrections, and source tips can be sent through the contact channels listed on our About page.