8GB · AI Score 2.0/100 · anchored estimate vs 51 measured cards
2 AI Score Includes estimates
We have not run NVIDIA GeForce RTX 2070 SUPER on our bench. These figures are anchored estimates, interpolated per workload against the 51 GPUs we did measure. On Llama 3.1 8B (Q4_K_M) NVIDIA GeForce RTX 2070 SUPER should deliver about 81 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 8GB. For image generation, SDXL should run near 1.11 it/s, while FLUX.1-dev won't fit at BF16 (needs ~26GB). 9 of the 12 workloads won't fit on 8GB at the tested precision, Qwen2.5-Coder 14B, Qwen3 32B, Llama 3.3 70B, Z-Image Turbo and others. We publish those as hard gates rather than quietly dropping to a smaller quant.
| Workload | Result | Telemetry | Data |
|---|---|---|---|
| Qwen3 4B | 126.4 tok/s | estimated | Est. |
| Llama 3.1 8B | 81 tok/s | estimated | Est. |
| Qwen2.5-Coder 14B | ✕ Won't fit | VRAM-gated at this precision | Est. |
| Qwen3 32B | ✕ Won't fit | VRAM-gated at this precision | Est. |
| Llama 3.3 70B | ✕ Won't fit | VRAM-gated at this precision | Est. |
| Workload | Result | Telemetry | Data |
|---|---|---|---|
| Stable Diffusion XL | 2.22 images/min | estimated | Est. |
| Z-Image Turbo | ✕ Won't fit | VRAM-gated at this precision | Est. |
| FLUX.1 dev | ✕ Won't fit | VRAM-gated at this precision | Est. |
| Workload | Result | Telemetry | Data |
|---|---|---|---|
| FLUX.1 Kontext dev | ✕ Won't fit | VRAM-gated at this precision | Est. |
| Qwen-Image-Edit | ✕ Won't fit | VRAM-gated at this precision | Est. |
| Workload | Result | Telemetry | Data |
|---|---|---|---|
| LTX-Video (distilled) | ✕ Won't fit | VRAM-gated at this precision | Est. |
| Wan 2.2 5B (720p) | ✕ Won't fit | VRAM-gated at this precision | Est. |
| Architecture | Turing (TU104) |
| CUDA cores | 2,560 |
| VRAM | 8GB GDDR6 |
| Memory bus | 256-bit |
| Memory bandwidth | 448 GB/s |
| Boost clock | 1,770 MHz |
| TDP | 215 W |
| Process | 12nm |
| Interface | PCIe 3.0 x16 |
| Release date | 2019-07-09 |
| Launch MSRP | $499 |
NVIDIA GeForce RTX 2070 SUPER scores 2.0/100, #85 of 102. It ran 3 of 12; 9 exceeded its 8GB. Figures are anchored estimates, not measurements, we flag that on every row.
100% = this card, AI & Machine Learning headline metric (AI Score). #45 of 61 desktop cards in this vertical.
| GPU | Relative | % | AI Score |
|---|---|---|---|
| NVIDIA GeForce RTX 3070 Ti | 110% | 2.2 | |
| NVIDIA GeForce RTX 2080 Super | 105% | 2.1 | |
| NVIDIA GeForce RTX 3070 Founders Edition | 105% | 2.1 | |
| NVIDIA GeForce RTX 5060 | 105% | 2.1 | |
| NVIDIA GeForce RTX 2070 SUPER | 100% | 2 | |
| NVIDIA GeForce RTX 2080 Founders Edition | 100% | 2 | |
| NVIDIA GeForce RTX 3060 Ti | 100% | 2 | |
| Intel Arc B580 | 100% | 2 | |
| NVIDIA GeForce RTX 2060 Super | 95% | 1.9 |
Same card, other workloads: NVIDIA GeForce RTX 2070 SUPER Gaming benchmarks
← All AI & Machine Learning GPU rankings
| Transistors | 13,600 million |
| Die size | 545 mm² |
| Fabricated by | TSMC |
| Transistor density | 25 million per mm² |
Denser than 30% of the 76 cards we have silicon data for. Density is the clearest measure of what a process node bought: a card that gained it without growing the die got its speed from the fab rather than the architecture.
Silicon figures from Wikipedia (CC BY-SA 4.0). Benchmarks on this page are our own. Compare every chip.
Whole-job timings, composed from our measured per-model results on this card.
Can't run: 60-second AI short film (needs Qwen3 32B), 60-second AI short film, narrated (needs Qwen3 32B), 10 short social clips (needs Qwen3 32B), 40-product photo shoot (needs FLUX.1 Kontext dev), 6-panel comic page (needs Qwen3 32B), 20 long-form articles (needs Llama 3.3 70B), Full codebase review (needs Qwen2.5-Coder 14B), Character sheet, 12 poses (needs FLUX.1 dev), 100-photo restoration batch (needs FLUX.1 Kontext dev), 24-frame storyboard (needs Qwen3 32B), 100-photo restore and enlarge (needs FLUX.1 Kontext dev).