12GB · AI Score 3.2/100 · first-party measured on 12 AI workloads
3.2 AI Score Includes estimates
Every number on this page is first-party: GeForce RTX 5070 was run on our pinned 12-workload AI suite on 2026-07-11, with under 0.5% run-to-run variance. On Llama 3.1 8B (Q4_K_M) GeForce RTX 5070 delivers about 119.87 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 12GB. For image generation, SDXL runs at 3.03 it/s, while FLUX.1-dev won't fit at BF16 (needs ~26GB). 9 of the 12 workloads won't fit on 12GB 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.
The 5070 pulled 101% of its 250W rating on Llama 8B. It will use every watt. The problem is the 12GB: 8 of 12 workloads gated, same wall as the 4070. One coder-14B result came back as a fail it shouldn't have been, so that's flagged pending a re-run. Quick note on the setup: all my AI benchmarking was done on rented cloud GPUs, I used all three of Vast.ai, RunPod and Modal depending on which had the card, and they all have their pros and cons. Same pinned harness on every run, and everything here got double-checked before it went up.
| Workload | Result | Telemetry | Data |
|---|---|---|---|
| Qwen3 4B | 180.21 tok/s | 2.6 GB peak132 W48°C1.36 tok/WQ4_K_M | ✓ Measured |
| Llama 3.1 8B | 119.87 tok/s | 4.6 GB peak210 W53°C0.57 tok/WQ4_K_M | ✓ Measured |
| Qwen2.5-Coder 14B | n/a tok/s | estimated | Est. |
| Qwen3 32B | ✕ Won't fit needs ~23 GB | VRAM-gated at this precision | ✓ Measured |
| Llama 3.3 70B | ✕ Won't fit needs ~46 GB | VRAM-gated at this precision | ✓ Measured |
| Workload | Result | Telemetry | Data |
|---|---|---|---|
| Stable Diffusion XL | 6.06 images/min | 7.5 GB peak189 W62°C9.9 s/img | ✓ Measured |
| Z-Image Turbo | ✕ Won't fit needs ~13 GB | VRAM-gated at this precision | ✓ Measured |
| FLUX.1 dev | ✕ Won't fit needs ~26 GB | VRAM-gated at this precision | ✓ Measured |
| Workload | Result | Telemetry | Data |
|---|---|---|---|
| FLUX.1 Kontext dev | ✕ Won't fit needs ~26 GB | VRAM-gated at this precision | ✓ Measured |
| Qwen-Image-Edit | ✕ Won't fit needs ~42 GB | VRAM-gated at this precision | ✓ Measured |
| Workload | Result | Telemetry | Data |
|---|---|---|---|
| LTX-Video (distilled) | ✕ Won't fit needs ~14 GB | VRAM-gated at this precision | ✓ Measured |
| Wan 2.2 5B (720p) | ✕ Won't fit needs ~18 GB | VRAM-gated at this precision | ✓ Measured |
| Architecture | Blackwell |
| CUDA cores | 6,144 |
| VRAM | 12GB GDDR7 |
| Memory bus | 192-bit |
| Memory bandwidth | 672 GB/s |
| Boost clock | 2,512 MHz |
| TDP | 250 W |
| Process | 4nm |
| Interface | PCIe 4.0 x16 |
| Release date | 2025-03-05 |
| Launch MSRP | $549 |
GeForce RTX 5070 scores 3.2/100, #64 of 102. It ran 3 of 12; 9 exceeded its 12GB. Every figure here is our own measurement.
100% = this card, AI & Machine Learning headline metric (AI Score). #27 of 61 desktop cards in this vertical.
| GPU | Relative | % | AI Score |
|---|---|---|---|
| AMD Radeon RX 6800 | 103% | 3.3 | |
| AMD Radeon RX 6900 XT | 103% | 3.3 | |
| AMD Radeon RX 7800 XT | 103% | 3.3 | |
| NVIDIA GeForce RTX 4060 Ti 16GB | 100% | 3.2 | |
| GeForce RTX 5070 | 100% | 3.2 | |
| NVIDIA GeForce RTX 4070 Super | 97% | 3.1 | |
| NVIDIA GeForce RTX 4070 | 97% | 3.1 | |
| NVIDIA GeForce RTX 4070 Ti | 94% | 3 | |
| Intel Arc A770 Limited Edition | 91% | 2.9 |
Same card, other workloads: GeForce RTX 5070 Gaming benchmarks
← All AI & Machine Learning GPU rankings
| Transistors | 31,100 million |
| Die size | 263 mm² |
| Process node | 4 nm |
| Fabricated by | TSMC |
| Transistor density | 118.3 million per mm² |
Denser than 74% 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), 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).