NVIDIA GeForce RTX 2060 Super
No editorial image yet — generic vendor mark shown. Credentials in spec table below.
Turing mid with the 8 GB upgrade — meaningful for AI. 7B Q4 fits comfortably with full context, 13B Q4 fits with offload. ~60-75 tok/s on 7B with ExLlamaV2. The '8 GB Turing' floor that many practical operators land on used.
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Sub-scores sum to 461 / 1000. Headline = 461 × 0.70 (Estimated-confidence discount) = 323. 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 448 GB/s bandwidth — 53.8 tok/s estimated. No measured benchmarks yet.
Plain-English: Comfortable for 7B chat.
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 reliable, budget-friendly entry into local inference with 7B models and occasional 13B experimentation. The 8 GB VRAM is the practical minimum for running 7B Q4 with full context (4K+ tokens) comfortably, and 13B Q4 fits with aggressive offloading to system RAM. On 7B Q4, expect 50-65 tok/s using ExLlamaV2 or llama.cpp, derived from the 448 GB/s bandwidth. The 2060 Super is a Turing-era card, so it lacks FP8/FP4 tensor core support, meaning no speedups from quantization formats newer than Q4. 13B Q4 runs at a slower 10-15 tok/s due to offloading overhead, and anything larger (e.g., 30B+) is impractical. Pass on this card if you need to run 13B models entirely in VRAM, or if you plan to work with 30B+ models at any usable speed. At ~$220 used, this is the cheapest 8 GB CUDA option that actually works for local AI without constant VRAM thrashing.
›Why this rating
The RTX 2060 Super earns a 6.5 because it hits the VRAM and bandwidth minimum for 7B Q4 inference at a low used price, but its Turing architecture lacks modern quantization support and cannot handle larger models smoothly. It's a capable starter card, not a long-term workhorse.
Overview
Turing mid with the 8 GB upgrade — meaningful for AI. 7B Q4 fits comfortably with full context, 13B Q4 fits with offload. ~60-75 tok/s on 7B with ExLlamaV2. The '8 GB Turing' floor that many practical operators land on used.
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Specs
| VRAM | 8 GB |
| Power draw (peak) | 175 W |
| Released | 2019 |
| MSRP | $399 |
| Backends | CUDA Vulkan |
Models that fit
Open-weight models small enough to run on NVIDIA GeForce RTX 2060 Super with usable context.
Frequently asked
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Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.