Editorial Policy

This page sets out how we make recommendations, handle affiliate relationships, publish corrections, and maintain editorial independence. It is the reference point for how Best GPU for AI operates.

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. If a $400 card is the right answer, we recommend the $400 card.

How we decide what to recommend

Every article follows the same internal standard:

• Reader intent first. We start from the question the reader is actually asking (budget, workload, model size) and work toward a GPU, not the other way around.
• VRAM matters most. For AI work, VRAM fit is the primary filter. We don't recommend a GPU that cannot comfortably run the target workload.
• Price-to-value honesty. Used-market alternatives (RTX 3090, RTX 3060 12GB) and cloud GPU rental are compared honestly, even when they reduce affiliate upside.
• One clear recommendation per question. If a reader asks "best GPU under $500," we name a specific card and defend it, rather than hedging with five options.

Sources and citations

We combine manufacturer specs, independent benchmark publications (Tom's Hardware, TechPowerUp, Phoronix, AnandTech), and community benchmark data (LocalScore, LM Studio community, Reddit benchmark threads). Full details on our Methodology page.

When a claim is specific (a benchmark number, a price, a compatibility detail), it comes from one of these sources. We avoid presenting community hearsay as fact.

Corrections policy

If we get something wrong, we want to fix it. Our corrections policy:

• Factual errors get corrected as soon as we are made 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 quietly rewrite them to hide errors.

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

Updates and freshness

GPU pricing, available models, and quantization techniques evolve quickly. We refresh articles when new hardware launches, when prices shift materially, or when a reader points out outdated information. We never bump dateModified on unchanged articles to appear fresh.

Content that we will not publish

• Articles written purely 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 at all).
• Fabricated benchmark numbers or expertise claims.

Affiliate relationships

We disclose every affiliate relationship. Active programs:

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

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, not a model's suggestion.

Contact

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