CanMyPCRunAILocal AI compatibility

What AI models can you run with 32GB of VRAM?

With 32GB of VRAM, 201 of 225 catalog builds have good/excellent reference fits at 4K context. 9 more run by offloading part of the model to system RAM, which works but is slower.

201

run well

9

with trade-offs

225

builds evaluated

Check my actual PC

Budget for context and concurrent work

The 32 GB reference is a single GPU capacity, such as a desktop RTX 5090. These calculations cover one loaded model; a second loaded model, concurrent requests or a larger context can consume the apparent headroom.

Quantization compresses weights; it does not remove context-cache or runtime costs. Compare the same model at Q4 and Q8 in the calculator, then validate its output on your task. Training and fine-tuning need a different memory budget.

Change system RAM and contextCompare exact builds8 GB vs 12 GB vs 16 GB

Four builds to compare by task

Editorial examples chosen for their documented tasks, all Q4_K_M at 4,096 tokens. Check the verdict before downloading. These are estimates, not benchmark rankings.

Short conversations and summaries

llama3.2:3b

A small instruction-tuned model for trying local chat with more memory headroom. Review summaries against the source text.

Task and build source · Catalog checked 2026-10-13

Excellent fit

Est. memory 2.9 GB · high confidence

Model fully fits in usable GPU VRAM.

ollama run llama3.2:3b

General instruction following

llama3.1:8b

An 8B instruction-tuned alternative to compare on your own prompts. It needs more memory than the 3B example and can still give incorrect answers.

Task and build source · Catalog checked 2026-10-13

Excellent fit

Est. memory 5.8 GB · high confidence

Model fully fits in usable GPU VRAM.

ollama run llama3.1:8b

Code explanation and editing

qwen2.5-coder:7b

A code-focused instruct build. Review generated changes and run your tests; a memory fit is not evidence that its code is correct.

Task and build source · Catalog checked 2026-10-13

Excellent fit

Est. memory 5.3 GB · high confidence

Model fully fits in usable GPU VRAM.

ollama run qwen2.5-coder:7b

A larger general-purpose comparison

qwen2.5:14b

A 14B instruct build for comparing answers when memory allows. Larger size does not establish better results for every task.

Task and build source · Catalog checked 2026-10-13

Excellent fit

Est. memory 10.2 GB · high confidence

Model fully fits in usable GPU VRAM.

ollama run qwen2.5:14b

After opening Ollama's prompt, enter /set parameter num_ctx 4096 to match this calculation, within the model's supported maximum. Install a current Ollama release that supports the build. Setup and troubleshooting.

Models that fit — largest memory first — for 32GB of VRAM

Heaviest first — each of these has a good/excellent reference fit with 32GB at 4K context, counting weights, KV cache, compute buffers and runtime overhead.

Models that fit in 32GB of VRAM
ModelBuildMemory neededVerdict
dolphin-mixtraldolphin-mixtral:8x7b-v2.6-q4_K_MQ4_K_M~29.5 GBGood fit
nous-hermes2-mixtralnous-hermes2-mixtral:8x7b-dpo-q4_K_MQ4_K_M~29.5 GBGood fit
mixtralmixtral:8x7b-text-v0.1-q4_K_MQ4_K_M~29.5 GBGood fit
nemotron-3.5-lightningnemotron-3.5-lightning:30bQ4_K_M~29.3 GBGood fit
nemotron-cascade-2nemotron-cascade-2:30bQ4_K_M~28.1 GBGood fit
notuxnotux:8x7b-v1-q4_K_MQ4_K_M~27.5 GBGood fit
alfredalfred:40bQ4_0Memory scenario exceeds documented maximum of 2,048 tokens.~24.8 GBGood fit
qwen3.6qwen3.6:27b-q4_K_MQ4_K_M~23.9 GBGood fit
codeboogacodebooga:34b-v0.1-q4_K_MQ4_K_M~21.5 GBExcellent fit
phind-codellamaphind-codellama:34b-python-q4_K_MQ4_K_M~21.5 GBExcellent fit
qwqqwq:32b-preview-q4_K_MQ4_K_M~21.4 GBExcellent fit
olmo-3.1olmo-3.1:32bQ4_K_M~21.0 GBExcellent fit
command-rcommand-r:35b-08-2024-q4_K_MQ4_K_M~20.9 GBExcellent fit
qwen3-coderqwen3-coder:30bQ4_K_M~19.4 GBExcellent fit
clefclef:27bQ4_K_M~18.5 GBExcellent fit
qwen3.8qwen3.8:27bQ4_K_M~18.3 GBExcellent fit
muse-glimmermuse-glimmer:30bQ4_K_M~17.4 GBExcellent fit
mistral-small3.1mistral-small3.1:24bQ4_K_M~16.6 GBExcellent fit
devstral-small-2devstral-small-2:24bQ4_K_M~16.3 GBExcellent fit
mistral-small3.2mistral-small3.2:24bQ4_K_M~16.3 GBExcellent fit
lfm2lfm2:24bQ4_K_M~16.1 GBExcellent fit
devstraldevstral:24bQ4_K_M~15.4 GBExcellent fit
magistralmagistral:24bQ4_K_M~15.4 GBExcellent fit
solar-prosolar-pro:22bQ4_K_M~15.0 GBExcellent fit
mistral-smallmistral-small:22b-instruct-2409-q4_K_MQ4_K_M~14.7 GBExcellent fit

Graphics cards with 32GB of VRAM

GeForce RTX 5090

How this was calculated

Every model is evaluated at 4K context against 32GB of VRAM, minus a reserve for the display and operating system, and assuming 32GB of system RAM with an illustrative 8-core/16-thread CPU and 500GB free disk. Available RAM and GPU budget/free memory are unknown. A working Vulkan backend on Windows is assumed; this is a capacity scenario, not a claim that all cards with this much memory have the same support. Your own machine may differ, which is what the PC scan measures directly.

32GB VRAM and local AI — frequently asked questions

Is 32GB of VRAM enough to run AI models locally?
Yes — 201 of the 225 builds evaluated have good/excellent reference fits with 32GB at 4K context, and 9 more run with part of the model offloaded to system RAM. The heaviest that fits is dolphin-mixtral:8x7b-v2.6-q4_K_M, needing about 29.5 GB.
How should I choose a model for 32GB VRAM?
Choose for your task, then check memory. dolphin-mixtral (dolphin-mixtral:8x7b-v2.6-q4_K_M) is the heaviest catalog default that fits this reference budget; its size does not establish answer quality. Smaller models leave headroom for a longer context window, which often matters more than raw parameter count.
Which graphics cards have 32GB of VRAM?
Cards with 32GB include GeForce RTX 5090.

Other VRAM amounts

4GB6GB8GB10GB12GB16GB24GB