Tech & Devices

Local AI Model Fit Finder

Estimate minimum VRAM from model size and quantisation, then shortlist GPUs and Mac configs that can hold the weights — with honest tight-fit caveats.

Start questions

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

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Local AI Model Fit Finder1 / 4

Roughly how large is the model you want to run?

Parameter count and quantisation provide a rough weight estimate. This finder adds a 10% context allowance and fixed 1GB runtime allowance, then calls a fit comfortable only below 80% of its estimated usable capacity. These are house heuristics, not measured or guaranteed runtime requirements.

Restoring saved answers

Choose an option to continue

Reference

What this tool can return

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

Comfortable Local Fit

Your answers favor hardware with headroom after weights load — not the minimum card that barely clears the arithmetic.

Best for:: Builders who will regret OOM errors more than paying for VRAM, Agent loops and longer context windows, 70B Q4 on 96GB workstation cards; 48GB discrete and 64GB Macs are tight, not comfortable
Watch out:: These are planning estimates, not runtime benchmarks. Quantisation format, backend, and context length still change real usage. Verify on the /gpus capacity tables before you buy.

Shortlist examples: RTX PRO 6000 Blackwell Workstation Edition, Apple M4 Max (64GB, previous generation), GeForce RTX 4090

Minimum Viable Hardware

Your answers accept a tighter fit or smaller models — the shortlist targets honest entry and mid-range capacity per pound.

Best for:: 7–8B Q4 on 8GB; 14B Q4 needs ~8.4GB so prefer 16GB, First local-AI experiments, Budget builds that still need 16GB not 8GB
Watch out:: Tight fits load weights then fail on long prompts. If you already know you want 70B comfortably, rerun with larger model size or comfort priority.

Shortlist examples: Radeon RX 9060 XT 16GB, GeForce RTX 5070 Ti, Apple M4 Pro (24GB, previous generation)

Rent Cloud Inference Instead

Your answers point past desk hardware — very large models or explicit preference for renting GPUs over buying cards.

Best for:: 100B+ or MoE models beyond consumer VRAM, Teams that prefer opex over another GPU purchase, Experiments before committing to hardware
Watch out:: Cloud $/hour and per-token costs need forecasting from your traffic shape. Use the GPU inference hub to pick marketplace vs API shape first.

Shortlist examples: RunPod, Together AI, CoreWeave

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 uses the same helpers as lib/gpus.ts:

Small / mid / large / extreme model size bands.
Capacity and comfort for headroom after weights load.
CUDA, AMD, Mac platform preferences.
Cloud-rent when desk hardware is the wrong buy.

Recommendations map to catalog GPUs and inference providers with merchant destinations. Pair results with the /gpus size tables before purchasing.

FAQ

Frequently asked questions

Is Q4 the right default?

For most local chat and coding helpers, yes. Q8 and FP16 need more VRAM — the quantisation question adjusts the planning estimate accordingly.

Will a tight fit work for agents?

Often no. Tight fits can load weights then fail on long context. Choose comfortable headroom if agent loops or long prompts are common.

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 Comfortable Local Fit, Minimum Viable Hardware, Rent Cloud Inference Instead 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.

Get Creator productivity pack

Supporting editorial

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

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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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Local AI Model Fit Finder

Roughly how large is the model you want to run?

What this tool can return

Comfortable Local Fit

Minimum Viable Hardware

Rent Cloud Inference Instead

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

Qwen Open Weights and Local AI — Which Qwen 3.8 Checkpoint You Can Actually Run

MacBook Air vs Windows Ultrabook for Students

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