NVIDIA GeForce GTX 1060 3GB
Pascal mid-range cut down to 3 GB VRAM. Below the practical AI floor — even 3B Q4 models need ~2 GB plus KV cache. Operators with this card almost universally pair it with CPU offload or upgrade. Still better than nothing for 1B model experiments.
Extrapolated from 192 GB/s bandwidth — 23.0 tok/s estimated. No measured benchmarks yet.
Plain-English: Doesn't fit modern chat models usefully — vision models won't fit.
Verdicts extrapolated from catalog VRAM + bandwidth + ecosystem flags. Hover any chip for the rationale. Want measured numbers? Submit your own run with runlocalai-bench --submit.
This card is for operators who already own one and want to tinker with sub-3B models, or for those building a dirt-cheap inference box for tiny experiments. It is not a serious local AI GPU in 2024.
What it runs well: 1B-2B parameter models at Q4 or Q8. A 1B Q4 (~0.7 GB) can hit ~150-200 tok/s from bandwidth, but the 3 GB VRAM ceiling means any model over ~2.5 GB forces CPU offload, cratering performance.
What breaks: Anything 3B or larger. A 3B Q4 (~2 GB) plus KV cache for 2048 context already pushes past 3 GB. 7B models are impossible without aggressive offload, dropping to <10 tok/s. No support for flash attention or modern inference optimizations.
When to pass: If the budget allows even $100 more, a used RTX 2060 6GB or GTX 1660 Super 6GB doubles VRAM and usable model range. Also pass if running any model above 3B parameters is the goal.
Price/value note: At ~$70 used, it is a cheap entry point for learning AI inference on a budget, but the 3 GB VRAM is a hard limit that makes it obsolete for most practical local AI workloads.
›Why this rating
The 3 GB VRAM is below the practical floor for most local AI models, limiting the card to tiny experiments. While cheap, it offers poor value per dollar compared to similarly priced 6 GB cards.
Overview
Pascal mid-range cut down to 3 GB VRAM. Below the practical AI floor — even 3B Q4 models need ~2 GB plus KV cache. Operators with this card almost universally pair it with CPU offload or upgrade. Still better than nothing for 1B model experiments.
Search-fallback links. Editorial hasn't yet curated retailer URLs for this card. Approx. $70.
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Specs
| VRAM | 3 GB |
| Power draw | 120 W |
| Released | 2016 |
| MSRP | $199 |
| Backends | CUDA Vulkan |
Models that fit
Open-weight models small enough to run on NVIDIA GeForce GTX 1060 3GB with usable context.
Frequently asked
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Where next?
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.