PC Parts Usable Value Comparator
Raw benchmark scores can make the most expensive card look smart. This view scores GPUs by the job you are actually buying for: gaming resolution, local AI, workstation work, used-card value, or low-power builds.
Loading dynamic parts-data.json...Why this score is different
Pick a use case to see how the score changes. The same GPU can be smart for local AI and a poor value for a simple 1080p gaming build.
Score ingredients
| # | GPU | Fit score | Price | Specs | Why it ranks here | Compare |
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Future-proofing reality check
Future proofing only pays when your real workload can use the extra performance before the card ages out of good value. For many buyers, a cheaper GPU replaced sooner beats paying a flagship premium today. The score flags likely overspend when a card has far more benchmark headroom than the selected use case needs.
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How to read a GPU comparison without being misled
A benchmark score is a single number describing how fast a card runs a specific test. It is useful, and it is also the reason people overspend. The score does not know your monitor, your workload, your power bill, or how long you intend to keep the card — and every one of those changes which GPU is actually the right purchase.
The comparator above scores cards against a selected job rather than against each other in the abstract. The sections below explain the reasoning behind each of the factors it weighs, so you can disagree with it on an informed basis.
Your resolution decides how much GPU you can use
Resolution is the first filter, because beyond a certain point the graphics card stops being what limits your frame rate.
- 1080p — the easiest case, and the one where expensive cards waste the most money. At this resolution a high-end GPU frequently finishes each frame before the processor can supply the next one, so you pay for performance the rest of the system cannot deliver. Mid-range is usually the sensible ceiling.
- 1440p — the resolution where GPU choice genuinely matters and where most of the value sits. Enough pixels to load the card properly, not so many that you need the top of the stack.
- 4K — where flagship cards finally justify themselves, and where VRAM and memory bandwidth start to bite as hard as raw shader throughput.
High refresh rates shift this upward: 1080p at 240 Hz asks more of a GPU than 1440p at 60 Hz. Match the card to the monitor you actually own, not the one you might buy later.
VRAM is a hard wall, not a performance dial
Most GPU specifications degrade gracefully — a slower card simply gives you fewer frames. Video memory does not work that way. When a workload exceeds available VRAM, performance does not dip, it collapses: stutter, texture pop-in, or an outright failure to run.
For gaming, the practical effect is that texture quality and ray tracing settings become unavailable rather than merely slow. For local AI work it is far more absolute, because a model that does not fit in memory does not run at usable speed at all. As a rough guide for quantized models at four bits:
- 7–8B parameters — roughly 5–6 GB, comfortable on an 8 GB card
- 13–14B — roughly 9–10 GB, wants 12 GB
- 30–34B — roughly 20 GB, wants 24 GB
- 70B — roughly 40 GB, which means dual cards or a workstation part
Add a couple of gigabytes on top for context and overhead, and more again for long context windows. This is why the comparator's AI use case weighs VRAM far more heavily than benchmark score: for that job a slower card with more memory beats a faster card with less, every time.
Power draw is a real cost, not a footnote
TDP affects three things beyond the electricity bill: the power supply you need, the heat your case has to move, and the noise you live with.
The running cost is easy to underestimate. A card drawing 250 W more than an alternative, used four hours a day, consumes about 365 kWh a year — roughly $60 a year at 17 cents per kilowatt-hour, and more in regions with higher rates. Over a four-year ownership that is a meaningful fraction of the price difference between tiers.
The power supply is the sharper constraint. A high-draw card demands both total wattage headroom and the right connectors, and transient spikes well above the rated TDP are what trip protection on a supply that looked adequate on paper. Budget generously, and treat a new PSU as part of the cost of a high-TDP card rather than a separate purchase.
Upscaling changes the comparison
Modern GPUs render at a lower internal resolution and reconstruct the image, and the quality of that reconstruction now varies enough between vendors and generations to outweigh a modest difference in raw performance. A card with weaker native benchmarks but better upscaling and frame generation can deliver the better experience at the resolution you actually play at.
Raw benchmark numbers usually measure native rendering, so they systematically understate cards with strong reconstruction. Worth remembering before treating a 10% score gap as decisive.
The used market, honestly
Used cards are frequently the best value on the board, and the risks are narrower than the folklore suggests.
A card used for mining is not automatically damaged — steady loads at moderate temperatures are gentler than repeated thermal cycling, and many such cards were undervolted. What genuinely ages is mechanical and consumable: fan bearings, thermal paste, and thermal pads on memory. Those are cheap to service and the usual reason an older card runs hot.
What to actually check: that the seller will power it up and show a display output, that fans spin without grinding, whether the warranty transfers with the card (policies differ by manufacturer), and that the physical card matches the model claimed. Run a stress test within any return window. And weigh the replacement cost of a failure against the saving — on a cheap card the discount usually justifies the risk, while on an expensive one it often does not.
Why future-proofing usually loses
The argument for buying above your needs is that the card lasts longer. The arithmetic rarely supports it.
Graphics cards depreciate steeply, and the flagship premium depreciates fastest, because it buys performance that only becomes necessary years later — by which point a mid-range card of a newer generation matches it for a fraction of the price. Two mid-range purchases spread across six years typically cost less in total than one flagship, and spend more of that time on current features and driver support.
Future-proofing is worth paying for when you have a specific, known workload that needs the headroom now — 4K at high refresh, large local models, professional rendering with real deadlines. It is a poor bet when the justification is a game that has not been announced.
The rest of the system matters
A fast GPU paired with an older processor produces a bottleneck that gets worse the lower your resolution, because low resolutions lean hardest on the CPU. Before upgrading the graphics card, check whether the frame rate you are unhappy with is actually GPU-limited: if usage sits well below 100% during the games that bother you, the problem is elsewhere and a new card will not fix it.
Also confirm physical fit — length, thickness in slots, and clearance against the front of the case — and that the case can move the heat. A card throttling in a badly ventilated case delivers less than its cheaper sibling in a well-ventilated one.
About the prices on this page
Pricing here is collected automatically from public sources and refreshed on a schedule. It is indicative rather than live: retail prices move daily, availability varies by region, and used-market estimates are inherently approximate. Check the source status note above for when the data was last refreshed, and confirm any figure with the retailer before buying on it.
FAQ
How much VRAM do I actually need?
It depends entirely on the job. For 1080p gaming, 8 GB is workable; for 1440p with high textures and ray tracing, 12 GB is the comfortable floor; for 4K, 16 GB and up. For local AI the requirement is absolute rather than gradual — a model that does not fit does not run usefully, so VRAM outranks raw speed.
What size language model fits in my GPU?
At four-bit quantization, roughly: a 7–8B model needs 5–6 GB, a 13–14B needs 9–10 GB, a 30–34B needs about 20 GB, and a 70B needs around 40 GB. Add a couple of gigabytes for context and overhead, and more for long context windows.
Is a faster card always better for AI work?
No. Memory capacity decides what you can run at all; speed only decides how quickly. A slower card with 24 GB will run models that a faster 12 GB card simply cannot load, which is why this page weights VRAM heavily in the AI use case.
Why does a high-end GPU not help much at 1080p?
Because at low resolutions the processor becomes the limit. The GPU finishes each frame before the CPU can supply the next, so extra graphics performance goes unused. If your GPU usage sits well below 100% in the games that bother you, a new card will not fix the frame rate.
Are used GPUs risky? What about mining cards?
Less than the folklore suggests. Steady mining loads at moderate temperatures are gentler on silicon than repeated thermal cycling, and many such cards were undervolted. What ages is mechanical — fan bearings, thermal paste, memory thermal pads — and those are cheap to service. Insist on seeing it powered with a display output, check whether the warranty transfers, and stress test within any return window.
How much does a power-hungry card really cost to run?
A card drawing 250 W more than the alternative, four hours a day, uses about 365 kWh a year — roughly $60 at 17 cents per kWh, more where electricity is dearer. Over four years that is a real share of the price gap between tiers, before counting the larger power supply and the extra heat and noise.
What power supply do I need?
Enough total wattage with headroom, and the right connectors. Transient spikes well above the rated TDP are what trip protection on a supply that looked sufficient on paper, so budget generously and treat a new PSU as part of the cost of a high-TDP card rather than a separate decision.
Do benchmark scores account for DLSS, FSR or XeSS?
Usually not — most measure native rendering. That systematically understates cards with strong upscaling and frame generation, which can deliver the better real experience despite a lower raw score. Treat a modest benchmark gap as less decisive than it looks.
Should I buy a more expensive card to future-proof?
Rarely. Flagship premiums depreciate fastest because they buy performance that only becomes necessary years later, by which time a newer mid-range card matches it for far less. Two mid-range purchases over six years usually cost less in total. Pay for headroom when you have a specific workload that needs it now, not for an unannounced game.
Why does the same GPU score differently here under different use cases?
Because the weighting changes. Gaming at 1080p rewards price efficiency and penalises paying for unusable headroom; local AI rewards VRAM above all; a low-power build weighs TDP heavily. A card that is excellent value for one job can be a poor buy for another, which is the whole point of scoring against a job rather than in the abstract.
How current are the prices shown?
They are collected automatically from public sources on a schedule, so treat them as indicative rather than live. Retail prices move daily, availability is regional, and used estimates are approximate. The source status note above shows when the data was last refreshed — confirm with the retailer before buying on a figure.