Spaces:
Running
ZeroGPU clone fails during tensor packing with Errno 28 โ 82.1 GB pack target
Hi Hugging Face team,
Iโm running a ZeroGPU Space cloned from the official LTX-2.5 Space.
Clone: Thananon/ltx-2-5-distilled-zero
Official reference: Lightricks/LTX-2.5
The official Space is currently RUNNING, while the clone consistently reaches RUNTIME_ERROR.
Environment
Hardware: zero-a10g
Clone revision: fdfce6d50477b5b7011c0f4b29d060265b3651b0
spaces==0.51.1
Build completes successfully.
Model snapshot download completes (~30.8 GB).
Model initialization and .to("cuda") complete under ZeroGPU CUDA emulation.
Failure
The process fails during ZeroGPU tensor packing:
OSError: [Errno 28] No space left on device
Call path:
demo.launch()
โ spaces.zero.gradio
โ ZeroGPU startup
โ torch.pack()
โ pack_tensors()
โ os.posix_fallocate(fd, 0, total_asize)
Packing reports:
0.00 / 82.1G
The failure occurs before the Space becomes operational.
We also checked spaces 0.51.3; the relevant packing behavior appears unchanged.
Question
Could you please clarify:
Is an ~82.1 GB ZeroGPU tensor pack expected to fit within the current ZeroGPU runtime environment?
Does the official LTX-2.5 Space receive different ephemeral disk/storage handling from clones?
Is there a supported way to increase ZeroGPU ephemeral disk or avoid this packing failure?
Is this a known limitation/bug in ZeroGPU packing for large models?
Is there a recommended deployment pattern for cloning the official LTX-2.5 Space successfully on ZeroGPU?
We have already verified that this is not a build-stage failure and have avoided further code changes so the failure can remain reproducible.
Thank you.
Hugging Face's Spaces overview documents 50 GB of default ephemeral disk. If this clone inherits that baseline, an 82.1 GB posix_fallocate cannot succeed even before accounting for the 30.8 GB snapshot.
A useful comparison would be df -h plus the mount points for $HF_HOME, /tmp, and the pack file on both the clone and official Space. If those differ, this looks more like a storage/runtime difference than a clone-code regression. If they match, the packer may be materializing a much larger aligned representation and needs a documented size limit or a streaming path.