8GB · AI Score 1.7/100 · anchored estimate vs 51 measured cards
1.7 AI Score Includes estimates
We have not run NVIDIA GeForce GTX 1080 on our bench. These figures are anchored estimates, interpolated per workload against the 51 GPUs we did measure (confidence: moderate). On Llama 3.1 8B (Q4_K_M) NVIDIA GeForce GTX 1080 should deliver about 44.5 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 0.54 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 | 59.12 tok/s | 162 W43°CQ4_K_M | ✓ Measured |
| Llama-3.1-8B | 35.98 tok/s | 175 W50°CQ4_K_M | ✓ Measured |
| Qwen3 8B | 34.97 tok/s | 171 W56°CQ4_K_M | ✓ Measured |
| Gemma 4 12B | 22.65 tok/s | 177 W59°CQ4_K_M | ✓ Measured |
| Qwen2.5-Coder 14B | ✕ Won't fit | VRAM-gated at this precision | Est. |
| Qwen3 14B | ✕ Won't fit needs ~11 GB | VRAM-gated at this precision | Est. |
| Gemma 4 26B A4B | ✕ Won't fit needs ~20 GB | VRAM-gated at this precision | Est. |
| Qwen3 30B A3B | ✕ Won't fit needs ~20 GB | VRAM-gated at this precision | Est. |
| Gemma 4 31B | ✕ Won't fit needs ~22 GB | 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 |
|---|---|---|---|
| Sana 1.6B | ✕ Won't fit needs ~11 GB | VRAM-gated at this precision | Est. |
| Stable Diffusion XL | 1.08 images/min | estimated | Est. |
| PixArt-Sigma XL | ✕ Won't fit needs ~14 GB | VRAM-gated at this precision | Est. |
| FLUX.1 dev | ✕ Won't fit | VRAM-gated at this precision | Est. |
| Z-Image Turbo | ✕ 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 | Pascal |
| CUDA cores | 2,560 |
| VRAM | 8GB GDDR5X |
| Memory bus | 256-bit |
| Memory bandwidth | 320 GB/s |
| Boost clock | 1,733 MHz |
| TDP | 180 W |
| Process | 16nm |
| Interface | PCIe 3.0 x16 |
| Release date | 2016-05-27 |
| Launch MSRP | $599 |
NVIDIA GeForce GTX 1080 scores 1.7/100, #94 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). #54 of 61 desktop cards in this vertical.
| GPU | Relative | % | AI Score |
|---|---|---|---|
| GeForce RTX 4060 | 112% | 1.9 | |
| NVIDIA GeForce RTX 5050 | 112% | 1.9 | |
| NVIDIA GeForce RTX 3050 | 106% | 1.8 | |
| NVIDIA GeForce GTX 1070 Ti | 100% | 1.7 | |
| NVIDIA GeForce GTX 1080 | 100% | 1.7 | |
| AMD Radeon RX 6700 | 94% | 1.6 | |
| AMD Radeon RX 7600 | 94% | 1.6 | |
| NVIDIA GeForce GTX 1660 Super | 94% | 1.6 | |
| NVIDIA GeForce GTX 1660 Ti | 94% | 1.6 |
Same card, other workloads: NVIDIA GeForce GTX 1080 Gaming benchmarks
← All AI & Machine Learning GPU rankings
| Transistors | 7,200 million |
| Die size | 314 mm² |
| Process node | 16 nm |
| Fabricated by | TSMC |
| Transistor density | 22.9 million per mm² |
Denser than 62% of the 746 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: Product photo shoot (needs FLUX.1 Kontext dev), Photo restoration batch (needs FLUX.1 Kontext dev), Restore and enlarge photos (needs FLUX.1 Kontext dev), Character sheet, 12 poses (needs FLUX.1 dev), Short social clips (needs Qwen3 32B), 60-second AI short film (needs Qwen3 32B), 24-frame storyboard (needs Qwen3 32B), 6-panel comic page (needs Qwen3 32B), Long-form article batch (needs Llama 3.3 70B), Full codebase review (needs Qwen2.5-Coder 14B).
This card is $599 to buy. The cheapest listed rate on Vast.ai is $0.049/hour, but that is the floor: we budget $0.059/hour, a 20% premium, because idle time, storage and unavailable cheap instances all land on the same bill. At that rate buying wins after 10,187 GPU-hours. Below it you are paying for idle silicon.
| How you would use it | GPU-hours a year | Rental cost a year | Time to break even |
|---|---|---|---|
| 2 hours a day, hobby | 730 | $43 | 14.0 years |
| 8 hours a day, working on it | 2,920 | $172 | 3.5 years |
| 24/7, always-on agent | 8,760 | $515 | 1.2 years |
At hobby usage this card is very unlikely to pay for itself before it is superseded. Rent it. Rental figures include a 20% premium over the cheapest listed rate. Ignores electricity, resale and the fact that a rented card can be a newer one tomorrow.
Cheapest of the RunPod and Vast on-demand rates we see, sampled daily. Spot and interruptible pricing runs lower.