MusicGen vs Jukebox vs Riffusion: Local Music AI Compared

If you’re running music generation locally, you’ve probably looked at MusicGen. It’s Meta’s text-to-music model, it runs on consumer hardware, and the output is genuinely usable. But it’s not the only option. I’ve spent the last few weeks running MusicGen against Jukebox and Riffusion on the same machine to see which one actually makes sense for a homelab setup. This isn’t about which one is “best”—it’s about which one solves your specific problem without eating your GPU memory or taking twelve hours to generate a thirty-second clip.

What These Tools Actually Do (and Don’t)

MusicGen takes a text prompt and generates music. You can give it “upbeat electronic dance track with heavy bass” or “lo-fi hip hop sample loops,” and it produces a full audio file. It also supports melody conditioning—you can hum or play a melody, and it’ll harmonize and arrange it. The model comes in different sizes: Small (300M parameters), Medium (1B), and Large (3.9B). It’s trained on licensed music, which matters if you care about legal concerns.

Jukebox, also from OpenAI, does something similar but older. It generates music conditioned on artist style and genre tags. You can’t really give it free-form descriptions the way you can with MusicGen. The output is higher fidelity—Jukebox trained on raw audio at higher sample rates—but the inference time is brutal. On a single 3090, a one-minute clip takes around twenty to thirty minutes.

Riffusion is different. It’s a fine-tuned Stable Diffusion model that works in the spectrogram domain—it generates spectrograms and converts them to audio. The pitch and timbre control is tighter than MusicGen’s, but the output quality is more hit-or-miss. Some prompts give you polished results. Others sound like a synthesizer having a seizure.

Hardware and Setup Requirements

This matters because it’s the first friction point. MusicGen’s Small model runs on CPU if you’re patient (around two minutes for a ten-second clip). The Medium model needs 8GB of VRAM for comfortable inference. The Large model wants 16GB+. I tested on a 3080 (10GB) and Medium mode was the sweet spot—inference took about fifteen to thirty seconds per ten-second clip.

Jukebox is much heavier. The smallest model wants 24GB of VRAM. A single inference pass takes significant time. I didn’t even try it on the 3080. This is where the comparison starts to matter—if you’ve got a 3090 or A100, Jukebox becomes viable. On consumer hardware, it’s a non-starter unless you’re willing to wait.

Riffusion is lightweight—runs on 2GB to 4GB of VRAM comfortably. Inference is faster than MusicGen. The tradeoff is quality and controllability. You’re working with diffusion steps and guidance scale, which gives you some precision but also some unpredictability.

Real numbers: MusicGen Medium on my 3080 generated a thirty-second clip in about forty-five seconds. Riffusion did it in eighteen seconds but needed three or four prompt iterations to get something usable. Jukebox would’ve taken eight to ten minutes, which is why I stopped testing it.

Installation and Integration

MusicGen has the cleanest setup. Clone the audiocraft repo from Meta, install dependencies, and you’re running inference with ten lines of Python. There’s also a Gradio interface if you want a web UI. Docker setup is straightforward—I’ve got it running under compose with GPU passthrough.

docker run --gpus all -it -p 7860:7860 
  -v $PWD/outputs:/app/outputs 
  ghcr.io/facebookresearch/audiocraft:latest

That’s not a real Dockerfile (I built my own), but the image exists and it works. Five minutes from clone to first generation.

Jukebox is… less friendly. You’re managing large checkpoint files. The codebase is less maintained. Installation is possible but you’re fighting with dependency versions. I got it working, but it felt fragile—like the kind of thing that would break after an upstream update.

Riffusion sits between them. It’s easier than Jukebox but less intuitive than MusicGen. The Hugging Face model downloads fine. Running inference requires some boilerplate. There’s a web interface if you want one, but it’s not as polished as MusicGen’s.

Output Quality and Use Cases

This is where your answer changes based on what you actually need.

MusicGen’s output is immediately useful. It’s clean, coherent, and sounds like intentional music rather than generated audio. Prompt quality matters—be specific about tempo, instruments, and mood. “Ambient meditation music with piano and strings, 60 BPM” gives you a consistent, listenable result. The melody conditioning feature is genuinely impressive; I hummed a melody on my phone, fed it the audio file, and got back a fully arranged version. Not perfect, but good enough for background music or demo tracks.

Jukebox produces higher-fidelity audio—the sample rate is higher, the spectral detail is there. But it’s trained on real songs, which means sometimes the output is too coherent—it starts feeling like a warped version of an existing track rather than original music. This matters if originality is important. Also, the conditioning is more limited. You’re choosing artist style and genre, not writing descriptions.

Riffusion’s strength is control. You can tweak seed values, guidance scale, and diffusion steps to dial in exactly what you want. But “dial in” is the operative phrase—you’re experimenting. It’s not push-button the way MusicGen is. The sound quality varies. Some generations are polished. Others are lo-fi in a way that sounds unintentional. I got good results for ambient backgrounds and experimental noise, but I’d hesitate to use it for anything client-facing without heavy cherry-picking.

Comparison Table

Feature	MusicGen	Jukebox	Riffusion
VRAM Required (min)	4GB (Small)	24GB	2GB
Inference Speed (30s clip)	45 sec (Medium)	8–10 min	18 sec
Prompt Style	Free-form text	Artist + Genre tags	Text prompts
Melody Conditioning	Yes, strong	No	Limited (seed-based)
Output Consistency	High	High	Medium
Quality Range	Good–Excellent	Very good–Excellent	Fair–Good
Setup Difficulty	Low	High	Medium
Active Development	Yes	Limited	Community-driven
Best For	Background music, demos	High-fidelity production	Experimental, control

When to Pick Each One

Use MusicGen if: You want something that works immediately. You’re generating background music, soundscapes, or jingles for projects. You want to condition on a hummed melody. You’ve got a 3080 or better but not exotic hardware. You care about setup time versus quality trade-off, and you’re willing to accept that generated music is generated music.

Use Jukebox if: You have a 24GB+ GPU sitting idle. You’re comfortable with long inference times. You specifically want high-fidelity output and you care about artist-style consistency. You need something that sounds production-ready without post-processing. Honestly, for most homelabs, this doesn’t apply. It’s a tool for when you have the hardware and the patience.

Use Riffusion if: You’re experimenting. You have a smaller GPU and want fast iteration. You like controlling diffusion parameters and working iteratively. You’re building something that rewards human curation—you can cherry-pick the good outputs and discard the rest. You don’t mind that setup is moderate friction.

The Friction I Hit

MusicGen’s melody conditioning doesn’t always preserve the exact pitch you hum. It interprets it, which is both a feature and a limitation. Some prompts generate repetitive loops instead of evolving structures. The audio is also relatively short—models are trained on clips up to thirty seconds, and going longer requires tiling or post-processing.

Jukebox’s checkpoint files are enormous. Just getting the model loaded is a production decision. And the inference time made testing impractical for quick iteration. If I needed Jukebox, I’d probably be running it on dedicated hardware that could afford to sit there for ten minutes per sample.

Riffusion’s spectrogram approach means you’re trusting the mel-spectrogram vocoder to convert back to audio cleanly. Sometimes artifacts creep in. The model is also smaller and less actively developed, which means edge cases don’t get caught. But for my use case—generating background loops and ambient texture—it’s fast enough that I can run multiple variations and pick the best one.

Integration with Your Homelab

If you’re running Home Assistant, Node-RED, or some other automation setup, MusicGen is the path of least resistance. The API is simple. Python bindings are straightforward. You can trigger music generation from automations easily. Riffusion is also manageable—both are light enough that you can run them on the same machine as other services without major contention.

Jukebox would need its own hardware or scheduled off-peak generation. It’s not something you’d run on demand during active hours unless your homelab is built around it specifically.

I ended up sticking with MusicGen for daily use and keeping Riffusion around for when I want faster iteration on experimental stuff. It’s not that Riffusion is worse—it just solves a different problem. MusicGen is the default. Riffusion is the specialty tool.

FAQ

Can I run all three on the same GPU?

Technically yes, but not simultaneously. You’d need to load and unload models. MusicGen and Riffusion share VRAM reasonably on a 10GB card. Jukebox is in a different category—you’re dedicating the GPU to it if you run it at all. For a homelab, you’d pick one and stick with it unless you have multiple GPUs.

Does MusicGen output quality degrade with longer clips?

Yes. The model trains on clips up to thirty seconds, so that’s the natural boundary. Longer generations often become repetitive or lose coherence. You can generate multiple thirty-second segments and stitch them, but that’s manual work.

Is the generated music copyright-free?

MusicGen trained on licensed music but generates original output—legally, it’s likely yours, but there’s no explicit guarantee. Jukebox has similar ambiguity. For client work or commercial use, you’re making a judgment call. For personal projects or testing, it doesn’t matter.

How much CPU fallback do I need if my GPU is busy?

MusicGen can run on CPU. A ten-second clip takes about two to three minutes on a modern multi-core CPU. It’s slow enough that you won’t do it by choice, but it works if your GPU is occupied. Riffusion is similar. Jukebox on CPU is impractical.

Which one integrates best with existing audio software?

All three output WAV or MP3. Integration is file-based—generate, save to a folder, consume elsewhere. If you need real-time streaming or plugin-style integration, none of these are there yet. For batch generation and workflow automation, they’re all equivalent once the audio is written to disk.

Explore MusicGen in our AI Homelab Toolkit.