NVIDIA GeForce RTX 2060
First consumer card with Tensor cores at the ~$200 used tier. 6 GB VRAM is the bottleneck — 7B Q4 fits with limited context. FP16/INT8 Tensor compute makes ExLlamaV2 actually fast (~50-70 tok/s on 7B). The 'minimum modern AI card' for many operators.
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Sub-scores sum to 367 / 1000. Headline = 367 × 0.70 (Estimated-confidence discount) = 257. 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 336 GB/s bandwidth — 40.3 tok/s estimated. No measured benchmarks yet.
Plain-English: Edge-of-fit for 7B; expect compromises.
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 the operator who needs a functional local AI rig at the lowest possible entry price. The RTX 2060 is the floor for running 7B models with Tensor core acceleration, making it a viable starter card for experimentation or lightweight inference.
On 7B Q4 models, the 336 GB/s bandwidth delivers 35-50 tok/s with ExLlamaV2, which is fast enough for interactive chat. Smaller 3B models run at 80+ tok/s. The 6 GB VRAM fits a 7B Q4 with a 2K-4K context window, but leaves no room for larger models or significant context expansion.
The 6 GB VRAM ceiling breaks any model above 7B — 13B models are out of reach entirely, and even 7B Q8 or 7B Q4 with 8K+ context will spill to system RAM, cratering performance. The card lacks FP8 or FP4 native support, so newer quantization formats may not run optimally.
Pass on this card if the workload requires running 13B models, long-context inference, or any multi-model setup. Operators with a budget above $250 should look at used RTX 3060 12GB or RTX 3070 for significantly more VRAM and bandwidth.
At $180 used, this is the cheapest entry point for Tensor core inference on 7B models. It is a stopgap, not a long-term solution.
›Why this rating
The RTX 2060 earns a 5.5 for local AI because it is the minimum viable card for 7B models with Tensor cores, but its 6 GB VRAM severely limits model size and context. It is a budget entry point, not a workhorse.
Overview
First consumer card with Tensor cores at the ~$200 used tier. 6 GB VRAM is the bottleneck — 7B Q4 fits with limited context. FP16/INT8 Tensor compute makes ExLlamaV2 actually fast (~50-70 tok/s on 7B). The 'minimum modern AI card' for many operators.
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Specs
| VRAM | 6 GB |
| Power draw (peak) | 160 W |
| Released | 2019 |
| MSRP | $349 |
| Backends | CUDA Vulkan |
Models that fit
Open-weight models small enough to run on NVIDIA GeForce RTX 2060 with usable context.
Frequently asked
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Does NVIDIA GeForce RTX 2060 support CUDA?
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Where next?
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.