GeForce RTX 5080 vs NVIDIA Titan RTX, AI & Machine Learning Comparison
GeForce RTX 5080 wins 16 of 24 benchmarks, averaging 49.3% faster.
Both cards' numbers are anchored estimates calibrated against our measured cards, pending first-party measurement. Treat small gaps as ties.
What the numbers say
The gap is widest in Stable Diffusion XL, where GeForce RTX 5080 leads by 87% (12.24 vs 6.56 images/min); the closest fight is Qwen3 4B (37% apart); VRAM decides part of this one: NVIDIA Titan RTX runs 34 of our 12 AI workloads while the other card runs 18, models that don't fit score zero.
Benchmark results head-to-head
| Benchmark | GeForce RTX 5080 | NVIDIA Titan RTX | Difference |
|---|---|---|---|
| MiniCPM5 2B tok/s | 367.12 | 225.16 | +63% |
| LFM2.5 2.6B tok/s | 377.14 | 244.88 | +54% |
| Granite 4.1 3B tok/s | 253.62 | 172.09 | +47% |
| Nemotron 3 Nano 4B tok/s | 255.18 | 166.76 | +53% |
| Qwen3 4B tok/s | 215.89 | 158.13 | +37% |
| DeepSeek Coder 7B Instruct v1.5 tok/s | 180.65 | 122.74 | +47% |
| Llama 3 8B tok/s | 158.84 | 108.26 | +47% |
| Llama 3.1 8B tok/s | 150.71 | 107.39 | +40% |
| Qwen3 8B tok/s | 153.89 | 104.36 | +47% |
| Nemotron Nano 9B v2 tok/s | 119.94 | 80.66 | +49% |
| Ornith 1.5 9B tok/s | 138.12 | 93.68 | +47% |
| Gemma 4 12B tok/s | 96.61 | 66.58 | +45% |
| Qwen2.5-Coder 14B tok/s | 81.97 | 59.75 | +37% |
| Qwen3 14B tok/s | 87.02 | 62.47 | +39% |
| Gemma 4 26B A4B tok/s | 0 | 119.86 | n/a |
| Qwen3 30B A3B tok/s | 0 | 156.15 | n/a |
| Qwen3 32B tok/s | 0 | 28.36 | n/a |
| Llama 3.3 70B tok/s | 0 | 0 | n/a |
| Stable Diffusion XL images/min | 12.24 | 6.56 | +87% |
| FLUX.1 Kontext dev images/min | 0 | 0 | n/a |
| FLUX.1 dev images/min | 0 | 0 | n/a |
| Qwen-Image-Edit images/min | 0 | 0 | n/a |
| LTX-Video (distilled) frames/s | 4.331 | 0 | n/a |
| Wan 2.2 5B (720p) frames/s | 0 | 0 | n/a |
Whole-job comparison
How long each card takes to finish a complete pipeline, not just one model. GeForce RTX 5080 is faster on 1 of 1; NVIDIA Titan RTX on 0.
| Workflow | GeForce RTX 5080 | NVIDIA Titan RTX | Difference |
|---|---|---|---|
| Full codebase review | 12.2 min | 16.7 min | GeForce RTX 5080 1.37x faster |
Specifications compared
| GeForce RTX 5080 | NVIDIA Titan RTX | |
|---|---|---|
| VRAM | 16GB | 24GB |
| Transistors | 45,600M | 18,600M |
| Die size | 378 mm² | 754 mm² |
| Process node | 4 nm | 12 nm |
| Transistor density | 120.6 M/mm² | 24.7 M/mm² |
| Architecture | Blackwell (GB203) | Turing (TU102) |
| Memory bandwidth | 960 GB/s | 672 GB/s |
| Boost clock | 2,617 MHz | 1,770 MHz |
| TDP | 360 W | 280 W |
| Launch MSRP | $999 | $2,499 |
| Release | 2025-01-30 | 2018-12-18 |
FAQ
- Which is better for ai & machine learning: GeForce RTX 5080 or NVIDIA Titan RTX?
- GeForce RTX 5080 performs better for ai & machine learning, winning 16 of 24 benchmarks in our suite with an average 49.3% advantage.
- What are the main hardware differences between GeForce RTX 5080 and NVIDIA Titan RTX?
- GeForce RTX 5080 has 16GB VRAM and a 360W TDP, while NVIDIA Titan RTX has 24GB VRAM and a 280W TDP.
- Does VRAM matter more than speed between GeForce RTX 5080 and NVIDIA Titan RTX?
- For AI, yes, NVIDIA Titan RTX fits 6 more of our 12 workloads than GeForce RTX 5080. A model that exceeds VRAM doesn't run slower, it doesn't run at all, so the card that fits the model wins that workload outright.
- Where is the biggest performance difference between GeForce RTX 5080 and NVIDIA Titan RTX?
- Stable Diffusion XL: GeForce RTX 5080 leads by roughly 87% (12.24 vs 6.56 images/min) in our testing.
GeForce RTX 5080 full review · NVIDIA Titan RTX full review · All AI & Machine Learning rankings