Datasets:
Making music on a gaming GPU: ACE-Step 1.5 on one RTX 5090
A full 4-minute song in under 2 seconds, on a consumer graphics card. ACE-Step 1.5 is an open-weights text-to-music model. Point it at one RTX 5090 (the current top gaming GPU) and it writes a complete 4-minute track in about 1.75 seconds of compute. That is roughly what the model's makers report for a datacenter A100, and about 6x faster than the last-gen RTX 3090 they also cite. These are the first numbers for this model on consumer Blackwell (sm_120).
The point of the benchmark is simple: you do not need a data center to make music with AI. A card you can buy for a gaming PC does it faster than the song plays.
The numbers
Two model sizes, both the fast "turbo" variant (8 diffusion steps). Each cell is 6 prompts across different genres, one seed, warm-up discarded.
| model | 30s song | 2-min song | 4-min song | vs real-time (4-min) | peak VRAM |
|---|---|---|---|---|---|
| 2B turbo | 0.37s | 0.92s | 1.75s | 137x faster | 9.4 GB |
| XL 4B turbo (higher quality) | 0.58s | 1.43s | 2.9s | 83x faster | 14.8 GB |
Times are generation compute (diffusion + audio decode), the same basis as the vendor's own figures. Writing the file to disk adds about 0.2 to 0.4s. Real-time factor (RTF) is song length divided by generation time: 137x means the 4-minute song is written 137 times faster than you could listen to it.
Where the 5090 lands. ACE-Step's team reports the 2B turbo at roughly 1 to 2 seconds per 4-minute song on an A100 80GB, and under 10 seconds on an RTX 3090. The 5090 comes in at 1.75s: level with the datacenter card, and far ahead of the 3090. And this is the plain out-of-box path (bf16, no torch.compile, no quantization), so it is a floor, not a ceiling.
Three things worth knowing
1. The fast tier is A100-class on a gaming card. 1.75s for a 4-minute song is the headline. A card built for games keeps pace with a card built for data centers, on a model anyone can download.
2. Quality costs time, and not much of it. The XL 4B model is the higher-quality tier. It takes about 1.65x longer than the 2B (2.9s vs 1.75s for a 4-minute song) and about 60% more memory. Still under 3 seconds, still on a single card. Listen to the paired samples and decide whether your ear wants the bigger model.
3. Longer songs are proportionally cheaper. RTF climbs from 81x at 30 seconds to 137x at 4 minutes for the 2B model. Because the step count is fixed at 8 regardless of length, the fixed overhead spreads thinner over a longer track. A 4-minute song is not 8x the work of a 30-second one; it is closer to 5x.
What we did not measure
Speed is the finding here. Audio quality is not scored: "which song sounds better" is a subjective call, and a made-up number would not help. Instead the run saves the actual songs, and the paired 2B-vs-XL clips are attached so you can judge by ear. A quality-alignment score (does the audio match the prompt) is the natural follow-up.
The run is DiT-only: the diffusion model generates directly from a text caption, with no planning language model in front. That matches how the vendor measured the speed claim, and it is the path most people will use.
Config
DiT-only, bf16, SDPA attention (flash-attn not required on sm_120), 8-step turbo, guidance off (turbo has no CFG), batch size 1, seed 0, 48 kHz stereo. RTX 5090 32GB, torch 2.10.0+cu128, ACE-Step 1.5 (v0.1.8). No torch.compile, no quantization. Donald (the box's resident model server) drained for the GPU window and restored after.
Reproduce
# capsule (RTX 5090), in the ACE-Step 1.5 clone + its uv env:
ACESTEP_INIT_LLM=false .venv/bin/python ace_synth.py \
--model-tier 2b --config-path acestep-v15-turbo \
--prompts prompts.json --durations 30,120,240 --steps 8 --seed 0 \
--out-dir ~/ace-out --synth-json ~/ace-out/synth-2b.json --save-audio
# (repeat with --model-tier xl --config-path acestep-v15-xl-turbo)
# Mac: aggregate + chart
python3 -m scripts.ace_bench --synth results/ace_step/synth-2b.json results/ace_step/synth-xl.json \
--out results/ace_step/ace-step-music.json
python3 scripts/chart_ace.py
Prompt set (dataset/ace_step/prompts.json): pop, lo-fi, orchestral, EDM, acoustic, hip-hop. Metric helpers lib/ace_step/ (RTF, aggregation) are unit-tested.
Model: ACE-Step 1.5 (Apache-2.0), paper arXiv 2602.00744. First RTX-5090/sm_120 figures.