Tech & Devices

GPU Finder

Match gaming, creative, or workstation needs — and local-model VRAM when that is your workload — to a graphics card that fits how you actually buy.

Start questions

Questions: 3–4 questions
Estimated time: 2 minutes
Result profiles: 3

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GPU Finder1 / 3

What is the main job for this GPU?

VRAM and software ecosystems diverge hard between games, creative apps, and local models. Pick the job that would make you regret the wrong card.

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Reference

What this tool can return

The 3 decision profiles behind the questions above, and who each one suits.

Local AI Capacity First

Your answers favor memory headroom for open models: buy VRAM (or usable unified memory) before chasing peak raster scores.

Best for:: Builders running mid-to-large open models locally, People who already know CUDA or MLX tooling, Buyers who will regret undersized VRAM more than lower frame rates
Watch out:: A 70B Q4 model is a ~42.5GB Q4_K_M file (~48GB with a modest context) — 24GB and 32GB discrete cards do not host it, and 48GB discrete or 64GB unified memory is a tight fit. Prefer 72GB+ discrete or 96GB+ unified memory for comfortable headroom; street prices in September 2026 run far above launch MSRP, so verify them and power/cooling before you buy.

Shortlist examples: RTX PRO 6000 Blackwell, GeForce RTX 5090, GeForce RTX 4090

Gaming and Creative Performance

Your answers favor frame rates, upscaling, and creative GPU features over maximum model capacity.

Best for:: 1440p / high-refresh gamers, Creators who also dabble in local image or mid-size LLM tools, Builders who want DLSS or FSR more than flagship VRAM
Watch out:: 16GB is the ceiling for many mid-size models once context grows. If 70B local inference is the real goal, switch to the capacity-first profile instead of buying a fast 16GB gaming card.

Shortlist examples: GeForce RTX 5080, GeForce RTX 5070 Ti, Radeon RX 9070 XT

Value and Capacity per Pound

Your answers favor spending less while still buying memory that will not feel obsolete in a year.

Best for:: First discrete upgrades and 1080p / light 1440p builds, Small local models and coding helpers, Builders following the entry PC builds parts list
Watch out:: 8GB is a hard ceiling for modern titles at high settings and for anything beyond small local models. If you already know you want 32B+ comfortably, stretch the budget instead of forcing an entry card.

Shortlist examples: Radeon RX 9060 XT 16GB, GeForce RTX 5060, GeForce RTX 4060 Ti 16GB

Compare head-to-head

The two finalists of each result profile in this tool, side by side.

Scoring methodology

How this engine scores fit

This tool weighs practical hardware signals:

Gaming for frame rates, upscaling, and ray tracing features.
Creative for GPU-accelerated editing and 3D pipelines.
Local AI when inference and agent workloads live or die on memory headroom.
Capacity for usable VRAM / unified memory after OS overhead.
CUDA ecosystem when NVIDIA tooling is a hard requirement.
AMD value when £-per-frame or £-per-GB beats CUDA exclusivity.
Mac unified when the buyer wants Apple silicon, not a desktop card.
Value for honest performance per pound on a strict budget.

Workload, budget, and platform preference each feed the score map. Local-model ambition only appears when that workload is selected. The strongest weighted profile becomes the recommendation, with alternates shown for comparison. Pair results with the /gpus buying guide before you buy.

FAQ

Frequently asked questions

Will a 24GB card run a 70B model?

Not at Q4. A 70B model needs about 35GB of weights (~42GB with runtime overhead) before long context. Treat 24GB as a mid-size ceiling, not a 70B host — see the GPU hub arithmetic for the exact sizes.

Should I buy NVIDIA just for local AI?

Usually yes if you want the path of least resistance: CUDA tooling is what most guides assume. AMD can win on gaming value or 24GB street prices, but ROCm support is the ongoing trade-off. Macs trade gaming leadership for large unified-memory configs.

Is 8GB enough in 2026?

For 1080p gaming and 7–8B Q4 helpers, often yes. 14B Q4 needs ~8.4GB of weights and does not fit 8GB cards — stretch to 16GB when the budget allows.

Review model and decision paths

Who reviews the ranking logic, what it can't know, and the paths that make two buyers get different answers.

Scoring modelAI Choice Engine scoring methodology

What the score is built from

This tool currently compares Local AI Capacity First, Gaming and Creative Performance, Value and Capacity per Pound decision paths.

Fit before features

Each recommendation starts with the buyer's situation, operating constraints, and decision risk before comparing feature lists.

Trade-offs are visible

Every result should explain who should choose it, who should skip it, and what would make a runner-up smarter.

Shortlists stay practical

The site favors shortlist clarity, setup reality, renewal risk, and owner capacity over inflated all-in-one claims.

Scoring inputs

The scoring model uses the question flow, weighted result profiles, shortlist trade-offs, and supporting editorial pages rather than a single generic ranking.

Limits to check

Pricing, availability, trial terms, and support commitments can change. Confirm current vendor terms before buying.

Logic review cadence: Reviewed when tool logic, category assumptions, or major vendor positioning changes.

Decision paths

Different buyers should not get the same answer

Use these paths to decide whether your answers should favor speed, depth, control, or practical ownership.

Everyday portability

Optimize battery, screen, keyboard, and weight when the device travels daily.

Creative workload

Prioritize sustained performance, display quality, memory, and storage for media work.

Long-term value

Avoid overbuying specs that will not change the next 18 months of actual work.

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Supporting editorial

Longer reads that explain the trade-offs this tool decides between.

Comparison

Buy a GPU for Local AI or Rent GPU Inference — How to Decide

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Keep exploring

Explained methodology

Each tool and guide makes the decision criteria and fit logic visible.

Clear disclosure

Commercial relationships are disclosed so readers can judge with context.

Ongoing updates

Important guides and tools are reviewed as products and categories change.

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GPU Finder

What is the main job for this GPU?

What this tool can return

Local AI Capacity First

Gaming and Creative Performance

Value and Capacity per Pound

Compare head-to-head

How this engine scores fit

Frequently asked questions

Review model and decision paths

What the score is built from

Different buyers should not get the same answer

Supporting editorial

Buy a GPU for Local AI or Rent GPU Inference — How to Decide

MacBook Air vs Windows Ultrabook for Students

MacBook Pro vs ASUS ProArt P16: Which Creative Laptop Fit Is Better?

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