12GB · AI Score 3.5/100 · first-party measured on 12 AI workloads
3.5 AI Score ✓ Measured
Every number on this page is first-party: NVIDIA GeForce RTX 3080 Ti 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 Ti delivers about 144.11 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 12GB. 8 of the 12 workloads won't fit on 12GB at the tested precision, Qwen3 32B, Llama 3.3 70B, Z-Image Turbo, FLUX.1-dev and others. We publish those as hard gates rather than quietly dropping to a smaller quant.
This was my hottest run of the whole fleet, 86°C at a flat 350W on the coder model. It'll do the work, but plan cooling around sustained load, not gaming bursts. 12GB gates 8 of 12 workloads. 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 | 206.45 tok/s | 2.7 GB peak195 W66°C1.06 tok/WQ4_K_M | ✓ Measured |
| Llama 3.1 8B | 144.11 tok/s | 4.9 GB peak275 W73°C0.52 tok/WQ4_K_M | ✓ Measured |
| Qwen2.5-Coder 14B | 78.31 tok/s | 8.7 GB peak305 W86°C0.26 tok/WQ4_K_M | ✓ Measured |
| 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 |
|---|---|---|---|
| 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 | Ampere (GA102) |
| CUDA cores | 10,240 |
| VRAM | 12GB GDDR6X |
| Memory bus | 384-bit |
| Memory bandwidth | 912.4 GB/s |
| Boost clock | 1,665 MHz |
| TDP | 350 W |
| Process | 8nm |
| Interface | PCIe 4.0 x16 |
| Release date | 2021-06-03 |
| Launch MSRP | $1,199 |
NVIDIA GeForce RTX 3080 Ti scores 3.5/100, #55 of 102. It ran 3 of 12; 8 exceeded its 12GB. Every figure here is our own measurement.
100% = this card, AI & Machine Learning headline metric (AI Score). #19 of 61 desktop cards in this vertical.
| GPU | Relative | % | AI Score |
|---|---|---|---|
| NVIDIA RTX 4000 (Ada Generation) | 126% | 4.4 | |
| NVIDIA GeForce RTX 4070 Ti Super | 123% | 4.3 | |
| GeForce RTX 5060 Ti | 106% | 3.7 | |
| AMD Radeon RX 6950 XT | 100% | 3.5 | |
| NVIDIA GeForce RTX 3080 Ti | 100% | 3.5 | |
| AMD Radeon RX 9070 XT | 97% | 3.4 | |
| AMD Radeon RX 9070 | 97% | 3.4 | |
| AMD Radeon RX 6800 XT | 94% | 3.3 | |
| AMD Radeon RX 6800 | 94% | 3.3 |
Same card, other workloads: NVIDIA GeForce RTX 3080 Ti Gaming benchmarks
← All AI & Machine Learning GPU rankings
| Transistors | 28,300 million |
| Die size | 628.4 mm² |
| Fabricated by | Samsung |
| Transistor density | 45 million per mm² |
Denser than 49% 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.
| Workflow | Time | Energy | Basis |
|---|---|---|---|
| Full codebase review | 12.8 min | 64.96 Wh | measured |
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), 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).
This card is $1,199 to buy. The cheapest listed rate on RunPod is $0.180/hour, but that is the floor: we budget $0.216/hour, a 20% premium, because idle time, storage and unavailable cheap instances all land on the same bill. At that rate buying wins after 5,551 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 | $158 | 7.6 years |
| 8 hours a day, working on it | 2,920 | $631 | 1.9 years |
| 24/7, always-on agent | 8,760 | $1,892 | 7.6 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.