Best GPU for Local AI

Start with whether the model fits

Local inference fails as memory planning first. At Q4, a rough planning rule is parameters × bits ÷ 8, plus headroom for context and the OS. A 70B model needs about 42GB under that heuristic — which is why a 24GB card is a mid-size workhorse, not a 70B free pass.

Quantization and context length change the answer more than brand loyalty. A 7B–13B chat model at Q4 fits comfortably on 8–12GB with room for a long prompt. A 32B–34B coding model wants 20GB+ before you start fighting swap. Multi-modal or long-context workloads eat VRAM that parameter-count tables never show. Write down the largest model you actually intend to keep resident, then size the card to that — not to a leaderboard you will never run.

Capacity tiers that match real workloads

Treat these as planning buckets, not product rankings:

• 12–16GB — small and mid chat models, image tools that stay under the limit, light coding assistants. Fine for experimenting; painful if every useful model spills to CPU RAM.
• 24GB — the practical sweet spot for many builders: mid-size open weights, batch jobs, and a second model occasionally in memory. Still the wrong expectation for comfortable single-GPU 70B.
• 48GB+ discrete or 64GB+ unified memory — where 70B-class and long-context work stop being a science project. A 32GB flagship (for example RTX 5090) is still short of the ~42GB Q4 weight floor once OS and context land. Apple Silicon with 64GB+ unified memory is often cheaper capacity than chasing multi-GPU desktops.

A used 24GB Ada card can beat a new gaming SKU with less memory for local AI specifically. Do not buy a 16GB “faster” raster champion if your workload is “does the model fit.”

Then pick the ecosystem you can live with

CUDA remains the path most guides, Docker images, and tutorials assume. NVIDIA wins on “will this random repo run on day one,” which matters if you are not already fluent in ROCm debugging.

AMD often wins on street price per gigabyte — especially used 24GB cards — with thinner day-one tooling. That trade is acceptable when you already know the stack you will run and have verified ROCm or Vulkan paths for it. It is a bad surprise when your first three GitHub READMEs only list CUDA.

Apple unified memory trades discrete gaming leadership for large-model capacity inside a Mac. Buy that path when the Mac form factor and memory size are the product; do not buy it as a substitute for a Windows gaming PC that also “does some AI.”

Power, case, and cooling are not footnotes

Local AI cards that sit at high utilization for hours stress the PSU and thermals harder than a gaming session that spikes and recovers. Check connector count, case clearance, and whether the card will throttle under sustained load in your chassis. A card that fits the VRAM math but browns out the power supply is not a bargain.

Laptop GPUs with the same marketing name usually have less usable memory bandwidth and worse sustained clocks than the desktop twin. If the machine must be portable, size for laptop reality — not desktop charts.

What to judge before spending

• What is the largest model (parameters + quantization + context) you will keep loaded day to day?
• Is day-one CUDA compatibility worth more than raw GB per pound?
• Will this machine also game, or is it an inference box?
• New vs used: is software support still active for the generation you are buying?
• Desktop discrete, workstation, or Mac unified memory — which form factor you will actually keep on the desk?

Run the finder, then verify on the tables

Use the GPU finder for a shortlist keyed to your constraints, then confirm fit on the /gpus size tables and /macs capacity notes before you buy. Street prices move weekly; treat launch MSRP as an anchor only. If two cards both clear the VRAM bar, prefer the one whose software path you already use — capacity that sits idle behind broken drivers is still a failed purchase.