NVIDIA GeForce GTX 1060 3GB
No editorial image yet — generic vendor mark shown. Credentials in spec table below.
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.
Affiliate disclosure: as an Amazon Associate and partner of other retailers, we earn from qualifying purchases. The verdict on this page is our editorial opinion; affiliate links never influence what we recommend.
Sub-scores sum to 312 / 1000. Headline = 312 × 0.70 (Estimated-confidence discount) = 218. This is an algorithmic performance-tier score — distinct from, and often lower than, the editorial “Our verdict” below, which weighs value and real-world fit (especially for hardware we haven’t measured yet). How scoring works →
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.
Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.
Specs
| VRAM | 3 GB |
| Power draw (peak) | 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
What models can NVIDIA GeForce GTX 1060 3GB run?
Does NVIDIA GeForce GTX 1060 3GB support CUDA?
How much does NVIDIA GeForce GTX 1060 3GB cost?
Where next?
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.