Less than 24 GB of VRAM — quick reference

The requirement stays: an NVIDIA GPU with 24 GB (YuE2's validated setup) or an Apple Silicon
Mac with 32 GB+. This page is for people running on smaller cards anyway. Linux numbers are
measured (LINUX_CUDA.md); Windows numbers are estimates (§4).

1. Your card

GPU	Works?	What you get	What to set
24 GB+ (RTX 3090, 4090, 5090, …)	Yes	Everything	Nothing
16 GB (RTX 4060 Ti 16G, 4070 Ti Super, 4080, 5060 Ti 16G, 5070 Ti, 5080, …)	Yes	Everything, including the longest song the app can make (6 min) at any CFG	Nothing
12 GB (RTX 3060 12G, 4070, 4070 Super, 4070 Ti, 5070, …) — Linux or Docker	With limits	Songs of ~5 min at CFG 1.0 · songs ≤ 3 min at CFG 1.5 · longer at CFG 1.5: §3	MEMORY BUDGET 12, CFG SCALE empty
12 GB — Windows	Borderline	Songs ≤ 3 min at CFG 1.0 to start · CFG 1.5: FP8 or Docker (§3)	MEMORY BUDGET 12, CFG SCALE empty, max_tokens 4 500
8–11 GB (RTX 4060, 3070, 3080 10G, all RTX 20 series, …)	No	—	Rent a cloud GPU (RunPod, …) with the Docker image; 16 GB there is enough

2. Where to set it

1. Top right → STUDIO (the SONG view has no settings).
2. Settings rail (right) → MEMORY BUDGET → 12. Repeat after every launch — it resets to 24.
   The next GENERATE applies it (or click LOAD / APPLY).
3. 01 // GENERATE → CFG SCALE → leave empty (= 1.0).
4. Song length → ADVANCED // SAMPLING → semantic phase → max_tokens; the Estimated audio
   length line updates. Shorten the lyrics to match, or the song is cut off at the limit.

max_tokens	2 200 (Preview preset)	4 500	6 000	7 200	9 000 (default)
Song length	≈ 1:30	3:00	4:00	4:48	6:00

3. 12 GB: what you want → what to set

Start at the top; go down one row only if that row is not what you want.

You want	Set	Result
~5-min song, CFG 1.0	MEMORY BUDGET 12, nothing else	Works (measured, ~5 min per song on an NVIDIA L4)
CFG 1.5, song ≤ 3 min	+ max_tokens 4 500, shorter lyrics	Works (measured with a 2-min song)
CFG 1.5, song ≤ 4:45	+ max_tokens 7 200	Works with almost no margin; long lyrics or a long score can push it over
CFG 1.5, full length	QUANTIZATION → fp8 (RTX 40 series or newer; not RTX 30)	Works, ~4× slower (23 min instead of 5½ on an L4)
Windows: the Linux rows above	Docker image under Docker Desktop + WSL2	Linux numbers and speed

Every one of these changes (CFG, length, FP8, Windows vs Linux) turns the same seed into a
different song. That is normal.

4. Windows users, read this

Windows is not a supported platform. Install, launch and Cover were verified once (Windows 11,
RTX 2070 8 GB); the maintainer has not generated a song on Windows, so the Windows numbers on
this page are the Linux measurements plus the known Windows overhead. Reports are welcome.

What you see	Why	What to do
Slower than the Linux figures (~1.5×); STATUS shows backend=torch-eager (… cannot run FlashAttention …)	Windows builds of PyTorch have no FlashAttention, so the app uses the slower decoder — it also needs ~0.7 GB more VRAM, which is why 12 GB is "borderline"	Expected. For Linux speed and memory, run the Docker image under Docker Desktop + WSL2
Crash at the first generate step: USE_FLASH_ATTENTION was not enabled for build	App older than 0.9.0	Update (Pinokio: Update). 0.9.0+ switches decoder by itself
Crash at start (fcntl), or Cover fails with non-English folder names or lyrics	App older than 0.9.0	Update
A song takes hours; STATUS shows device=cpu	PyPI's Windows torch is CPU-only	Pinokio installs the CUDA build for you. Manual install, in the app's venv: pip install torch==2.10.0 --index-url https://download.pytorch.org/whl/cu128 --force-reinstall --no-deps
Out of memory on 12 GB with the §3 settings	The desktop, browser and other apps share the card	Close other GPU apps. If the CPU has built-in graphics, plug the monitor into the motherboard

5. Out of memory?

The app keeps running. Change one thing, press GENERATE again: on 12 GB, go one row down in §3;
on Windows, see the last row of §4.

6. Leave these alone

Setting	Why
OFFLOAD AR WEIGHTS	Does not lower the peak (measured)
ODE STEPS	Changes render time and sound, not memory
DTYPE, BACKEND, VAE CORE FRAMES	auto / torch / auto; the app picks the right path
MEMORY BUDGET below your card's size	Saves nothing; runs fail sooner