CanMyPCRunAILocal AI compatibility

What AI models can you run with 16GB of VRAM?

With 16GB of VRAM, 178 of 225 catalog builds have good/excellent reference fits at 4K context. 25 more run by offloading part of the model to system RAM, which works but is slower.

178

run well

25

with trade-offs

225

builds evaluated

Check my actual PC

Separate dedicated VRAM from a 16 GB Mac

Sixteen gigabytes of dedicated GPU memory is not equivalent to a Mac with 16 GB total unified memory. This page assumes separate system RAM. For a Mac, use the Apple Silicon guide so operating-system and GPU allocations come from one shared pool.

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 GB16 GB Apple Silicon guide

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 16GB of VRAM

Heaviest first — each of these has a good/excellent reference fit with 16GB at 4K context, counting weights, KV cache, compute buffers and runtime overhead.

Models that fit in 16GB of VRAM
ModelBuildMemory neededVerdict
solar-prosolar-pro:22bQ4_K_M~15.0 GBGood fit
mistral-smallmistral-small:22b-instruct-2409-q4_K_MQ4_K_M~14.7 GBGood fit
codestralcodestral:22b-v0.1-q4_K_MQ4_K_M~14.7 GBGood fit
gpt-ossgpt-oss:20bMXFP4~14.5 GBGood fit
gpt-oss-safeguardgpt-oss-safeguard:20bMXFP4~14.5 GBGood fit
phi4-reasoningphi4-reasoning:14bQ4_K_M~12.4 GBGood fit
deepseek-coder-v2deepseek-coder-v2:16b-lite-base-q4_K_MQ4_K_M~11.9 GBGood fit
deepseek-v2deepseek-v2:16b-lite-chat-q4_K_MQ4_K_M~11.9 GBGood fit
everythinglmeverythinglm:13b-16k-q4_K_MQ4_K_M~11.4 GBExcellent fit
nexusravennexusraven:13b-q4_K_MQ4_K_M~11.4 GBExcellent fit
open-orca-platypus2open-orca-platypus2:13b-q4_K_MQ4_K_M~11.4 GBExcellent fit
codeupcodeup:13b-llama2-chat-q4_K_MQ4_K_M~11.4 GBExcellent fit
wizard-vicunawizard-vicuna:13b-q4_K_MQ4_K_MMemory scenario exceeds documented maximum of 2,048 tokens.~11.4 GBExcellent fit
wizardlm-uncensoredwizardlm-uncensored:13b-llama2-q4_K_MQ4_K_M~11.4 GBExcellent fit
clef-flashclef-flash:9bQ8_0~11.0 GBExcellent fit
phi4phi4:14bQ4_K_M~10.3 GBExcellent fit
llama3.2-visionllama3.2-vision:11bQ4_K_M~8.9 GBExcellent fit
mistral-nemomistral-nemo:12b-instruct-2407-q4_K_MQ4_K_M~8.5 GBExcellent fit
falcon2falcon2:11b-q4_K_MQ4_K_MMemory scenario exceeds documented maximum of 2,048 tokens.~8.2 GBExcellent fit
llama-prollama-pro:8b-text-q4_K_MQ4_K_M~8.0 GBExcellent fit
nous-hermes2nous-hermes2:10.7b-solar-q4_K_MQ4_K_M~7.6 GBExcellent fit
solarsolar:10.7b-text-v1-q4_K_MQ4_K_M~7.6 GBExcellent fit
deepseek-ocrdeepseek-ocr:3bF16~7.4 GBExcellent fit
olmo2olmo2:7bQ4_K_M~6.9 GBExcellent fit
olmo-3olmo-3:7b-instruct-q4_K_MQ4_K_M~6.9 GBExcellent fit

Graphics cards with 16GB of VRAM

GeForce RTX 5080GeForce RTX 5070 TiGeForce RTX 5060 Ti 16GBGeForce RTX 4080GeForce RTX 4060 Ti 16GBRadeon RX 9070 XTRadeon RX 9060 XT 16GBRadeon RX 7800 XTRadeon RX 6800 XT

How this was calculated

Every model is evaluated at 4K context against 16GB 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.

16GB VRAM and local AI — frequently asked questions

Is 16GB of VRAM enough to run AI models locally?
Yes — 178 of the 225 builds evaluated have good/excellent reference fits with 16GB at 4K context, and 25 more run with part of the model offloaded to system RAM. The heaviest that fits is solar-pro:22b, needing about 15.0 GB.
How should I choose a model for 16GB VRAM?
Choose for your task, then check memory. solar-pro (solar-pro:22b) 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 16GB of VRAM?
Cards with 16GB include GeForce RTX 5080, GeForce RTX 5070 Ti, GeForce RTX 5060 Ti 16GB, GeForce RTX 4080, GeForce RTX 4060 Ti 16GB, Radeon RX 9070 XT, Radeon RX 9060 XT 16GB, Radeon RX 7800 XT, Radeon RX 6800 XT.

Other VRAM amounts

4GB6GB8GB10GB12GB24GB32GB