Our editorial philosophy

RunLocalAI exists to help operators run AI on hardware they control. We make money through affiliate links and ads (disclosed at how we make money) and we want to keep doing that, but the principles below are what the site is actually for. If they conflict with the easy-affiliate-revenue path, the principles win. We've turned down monetization patterns that would've boosted short-term revenue because they would've made us into a different kind of site — and that kind of site doesn't survive.

What we believe

1. Most local-AI users should not buy expensive hardware

The honest answer to "what should I buy for local AI?" is almost always cheaper than people expect. A used RTX 3090 at $700-900 outperforms most new sub-$1,500 cards at the workloads our readers actually run. A 4060 Ti 16 GB at $450-550 covers 90% of the 14B-class daily workflow. An M4 Max with 64 GB unified memory beats a $3,000 PC build for 70B inference if silence and simplicity matter. The expensive option is rarely the right answer; we say so.

2. We recommend used hardware aggressively

Used 3090s, used 4090s, used Mac Studios, ex-datacenter A6000s — the secondary market for AI hardware is genuinely good in 2026, and used cards beat new-card $/GB-VRAM by meaningful margins. Some publications avoid recommending used because Amazon affiliate commissions on new cards are higher. We accept the lower per-click revenue on used recommendations because it's the honest answer for the buyer's actual situation. See our best used GPU for local AI guide for the framework.

3. We tell people not to upgrade more often than we tell them to

If you're already on a 3060 12 GB and your workload is 7-13B chat, we will tell you to keep it. If you're considering a 4090 because you saw a YouTube video and your daily use is ChatGPT-style chat, we will tell you the cloud subscription is better-priced. If you want to spend $2,500 on a 5090 and you don't run image generation or 70B-class models, we will tell you the upgrade is wasted money. Anti-upgrade guidance is the most under-served editorial direction in AI hardware coverage. We over-index on it intentionally.

4. We don't rank picks by affiliate commission

Buyer-guide rankings on this site are decided before any affiliate-link consideration. The decision lens is workload fit and buyer leverage at the actual budget, not which card has the highest Amazon Associates commission rate. A card with no affiliate program at all can still be the recommended pick — we just link to it without a commission and say so.

5. We don't optimize for the highest-priced GPU

The 5090 makes us the most affiliate revenue per click. The used 3090 makes us a fraction of that. The recommended pick for most readers is still the used 3090, because it's the right answer for their workload. We're aware that this hurts per-click revenue. We accept the trade because we don't want to become the kind of site that over-recommends expensive hardware to score commissions.

6. Workload fit beats prestige

A 4060 Ti 16 GB running Llama 3.3 70B Q4 at 12 tok/s is a better tool for a daily-summary workflow than a 5090 running the same model at 28 tok/s — because the human reads at ~10 tok/s. The extra speed doesn't translate to user experience. We frame picks by what the workload actually demands, not what the spec sheet looks impressive doing.

7. We frequently recommend cloud over local

Local AI isn't always the right answer. If your workload is bursty (4 hours/month), cloud rental at $0.50-1.50/hour costs $2-6/month — local hardware doesn't amortize. If you need the latest research model on day-zero, NVIDIA cloud instances get vendor support before the local-AI ecosystem catches up. If you need to share access with a small team, a cloud-hosted model serves multiple users better than a single-GPU desktop. We say so explicitly on every guide where it applies.

8. We over-explain operational realities

Spec sheets compare cards on tok/s and TFLOPS. Real workloads live in thermal throttling, KV cache exhaustion at 32K context, driver-version regressions, PSU sizing, and the 12VHPWR connector reliability conversation. We write about these things at length because they're the things that determine whether the hardware you bought is actually doing what you wanted. Our verdict pages have "what breaks first" sections precisely because spec-sheet-only coverage is the source of most buyer regret.

9. We refuse fake aggregate ratings and benchmark cherry-picking

You will not see "9.2/10 from 142 reviews" on this site. We don't have 142 reviews; the line would be a lie. We don't publish AggregateRating structured data unless we have real reviews. We don't cherry-pick benchmarks to make a card we recommend look better; when we recommend the 3090 over the 4090, we say the 4090 is faster and explain why the speed difference doesn't matter for the workload. The structured Pros/Cons schema we ship is drawn from real editorial whoIsItFor / whoShouldSkip arrays — never invented for SERP appearance.

What this means for you

When a page on this site recommends a $2,200 card, that recommendation is for buyers whose workload genuinely needs it. When the same page tells someone else to skip the card, that section is doing the more important work. Our buyer guides have explicit "who should skip this" sections because a non-buyer leaving the page with the right decision is a win — not a lost click.

If you read a page on this site and decide not to buy anything, the page worked. The right hardware decision is often "the hardware you already own is enough" or "wait another six months" or "rent cloud GPU for this one task." We try to make that explicit on every page where it applies.

How to call us out

If a recommendation on this site conflicts with these principles — if we look like we're pushing a more-expensive card than the workload needs, or skipping the cloud option when cloud would obviously be better, or burying a "skip this" answer that should be the top recommendation — please email us. We update pages quickly when challenged with operator-grade reasoning. The audit trail is at contact.

See also: editorial policy (process), how we make money (disclosure), methodology (testing).