10GB · AI Score 2.3/100 · first-party measured on 12 AI workloads
2.3 AI Score Includes estimates
Every number on this page is first-party: NVIDIA GeForce RTX 3080 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) NVIDIA GeForce RTX 3080 delivers about 125.85 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 10GB. For image generation, SDXL runs at 2.38 it/s, while FLUX.1-dev won't fit at BF16 (needs ~26GB). 9 of the 12 workloads won't fit on 10GB 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 3080 sat at exactly its 320W limit on Llama 8B, it uses everything it's given. The thing that surprised me is how little the 10GB buys you over the 8GB cards for AI: still 8 of 12 workloads gated. One run (Qwen2.5-Coder 14B) came back as a fail that shouldn't have been, so that one's flagged until I re-run it. 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 |
|---|---|---|---|
| MiniCPM5 2B | 272.23 tok/s | 180 W46°CQ4_K_M | ✓ Measured |
| LFM2.5 2.6B | 279.55 tok/s | 177 W44°CQ4_K_M | ✓ Measured |
| Granite 4.1 3B | 204.19 tok/s | 184 W49°CQ4_K_M | ✓ Measured |
| Nemotron 3 Nano 4B | 189.44 tok/s | 189 W46°CQ4_K_M | ✓ Measured |
| Qwen3 4B | 184.08 tok/s | 2.9 GB peak190 W63°C0.97 tok/WQ4_K_M | ✓ Measured |
| DeepSeek Coder 7B Instruct v1.5 | 141.63 tok/s | 204 W48°CQ4_K_M | ✓ Measured |
| Llama 3 8B | 125.8 tok/s | 205 W48°CQ4_K_M | ✓ Measured |
| Llama 3.1 8B | 125.85 tok/s | 4.9 GB peak250 W66°C0.5 tok/WQ4_K_M | ✓ Measured |
| Qwen3 8B | 120.85 tok/s | 205 W48°CQ4_K_M | ✓ Measured |
| Nemotron Nano 9B v2 | 91.15 tok/s | 210 W49°CQ4_K_M | ✓ Measured |
| Ornith 1.5 9B | 107.51 tok/s | 207 W48°CQ4_K_M | ✓ Measured |
| Gemma 4 12B | 76.61 tok/s | 214 W49°CQ4_K_M | ✓ Measured |
| Qwen2.5-Coder 14B | ✕ Won't fit needs ~12 GB | VRAM-gated at this precision | ✓ Measured |
| Qwen3 14B | 71.02 tok/s | 221 W49°CQ4_K_M | ✓ Measured |
| 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 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 | 4.76 images/min | 9.1 GB peak268 W71°C12.6 s/img | ✓ Measured CPU offload |
| Z-Image Turbo | ✕ Won't fit needs ~13 GB | VRAM-gated at this precision | ✓ Measured |
| PixArt-Sigma XL | ✕ Won't fit needs ~14 GB | VRAM-gated at this precision | Est. |
| 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 | Ampere (GA102) |
| CUDA cores | 8,704 |
| VRAM | 10GB GDDR6X |
| Memory bus | 320-bit |
| Memory bandwidth | 760 GB/s |
| Boost clock | 1,710 MHz |
| TDP | 320 W |
| Process | 8nm |
| Interface | PCIe 4.0 x16 |
| Release date | 2020-09-17 |
| Launch MSRP | $699 |
NVIDIA GeForce RTX 3080 scores 2.3/100, #74 of 102. It ran 3 of 12; 9 exceeded its 10GB. Every figure here is our own measurement.
100% = this card, AI & Machine Learning headline metric (AI Score). #35 of 61 desktop cards in this vertical.
| GPU | Relative | % | AI Score |
|---|---|---|---|
| AMD Radeon RX 6800 | 100% | 2.3 | |
| AMD Radeon RX 6900 XT | 100% | 2.3 | |
| AMD Radeon RX 6950 XT | 100% | 2.3 | |
| GeForce GTX 1080 Ti | 100% | 2.3 | |
| NVIDIA GeForce RTX 3080 | 100% | 2.3 | |
| NVIDIA TITAN Xp | 100% | 2.3 | |
| AMD Radeon RX 7700 XT | 96% | 2.2 | |
| NVIDIA GeForce RTX 2080 Ti Founders Edition | 96% | 2.2 | |
| NVIDIA GeForce RTX 3070 Ti | 96% | 2.2 |
Same card, other workloads: NVIDIA GeForce RTX 3080 Gaming benchmarks
← All AI & Machine Learning GPU rankings
| Transistors | 28,300 million |
| Die size | 628.4 mm² |
| Process node | 8 nm |
| Fabricated by | Samsung |
| Transistor density | 45 million per mm² |
Denser than 81% 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 $699 to buy. The cheapest listed rate on Vast.ai is $0.109/hour, but that is the floor: we budget $0.131/hour, a 20% premium, because idle time, storage and unavailable cheap instances all land on the same bill. At that rate buying wins after 5,344 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 | $95 | 7.3 years |
| 8 hours a day, working on it | 2,920 | $382 | 1.8 years |
| 24/7, always-on agent | 8,760 | $1,146 | 7.3 months |
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.