Nvidia GPU Benchmark Scores In AI Image Generation scores help show which cards are actually faster for models like Stable Diffusion, SDXL, and FLUX.
RTX 5090 and RTX 5080 show the fastest results in AI image-generation testing. This is very obvious as currently they are the most high-performance GPUs available to consumers.
Table of Contents
Units For Benchmarking
- SD 1.5 means the benchmark is running Stable Diffusion 1.5, a popular text-to-image model.
- FP16 means the model is using 16-bit floating point math, which is faster and uses less memory than full precision while keeping good image quality.
- INT8 means the model is using 8-bit integer quantization, which can reduce memory use and sometimes increase speed, but it is more aggressive and may change performance or quality depending on the implementation.
So when you see:
- SD 1.5 FP16 score = benchmark result for Stable Diffusion 1.5 running in 16-bit floating point mode.
- SD 1.5 INT8 score = benchmark result for Stable Diffusion 1.5 running in 8-bit integer mode.
FP16 and INT8 do not measure the exact same run conditions, so the scores can’t be treated as identical. FP16 is usually the more standard “high-quality fast inference” mode, while INT8 is more compressed and may behave differently across GPUs and software stacks.
- FP16 = usually more faithful, often the default for serious AI work.
- INT8 = more compressed, often faster or lighter, but more dependent on optimization.
So a GPU’s SD 1.5 FP16 score tells you how well it handles the model in a common high-performance mode, while the SD 1.5 INT8 score tells you how well it performs under a more optimized, lower-precision setup.
RTX 50 Series Scores
The GPU benchmark scores in AI image generation show that the RTX 5090 leads, followed by the RTX 5080.
| GPU | SD 1.5 FP16 score | SD 1.5 INT8 score | SDXL FP16 score |
|---|---|---|---|
| RTX 5090 | 8,193 | 79,272 | 7,179 |
| RTX 5080 | 4,650 | 55,683 | 4,257 |
| RTX 4090 | 5,260 | 62,160 | 5,025 |
| RTX 6000 Ada | 4,230 | 55,901 | 3,043 |
What the scores show
The RTX 5090 is the strongest card in the set, and the RTX 5080 is also very capable for AI image generation. In some tests, the RTX 5080 competes closely with or even beats the RTX 6000 Ada depending on the model and precision mode.
There are also timing-based results. For example, the RTX 5080 was reported at about 0.561 seconds per image in one Stable Diffusion 1.5 INT8 run, while the RTX 5090 was around 0.394 seconds per image in the same style of test.
Benchmarks Variations
These scores are not universal. A GPU can rank differently depending on several factors. Some of them are:
- Mmodel used.
- Image resolution.
- Precision mode like FP16, INT8, or FP4.
- Sampler and step count.
That is why one benchmark may show raw scores, while another shows time per image or relative speedup instead.
Nvidia GPU Benchmark Scores In AI Image Generation
AI image generation is a useful way to measure GPU performance because it tests compute power, memory, and efficiency together. Unlike gaming, these workloads show how quickly a GPU can turn prompts into finished images.
The main ways people benchmark this are:
- Overall benchmark scores.
- Seconds per image.
- Images per minute.
- Steps per second.
- Power efficiency, such as images per watt.
Takeaways
The RTX 5090 is the most cost-effective GPU in this case.
The best way to judge a GPU for this work is to look at both:
- benchmark score,
- and actual image-generation time.
That gives a much better picture than gaming performance alone
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