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cbensimonย 
posted an update 16 days ago
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๐Ÿš€ ZeroGPU now supports PyTorch native quantization via torchao

While it hasnโ€™t been battle-tested yet, Int8WeightOnlyConfig is already working flawlessly in our tests.

Let us know if you run into any issues โ€” and weโ€™re excited to see what the community will build!

import spaces
from diffusers import FluxPipeline
from torchao.quantization.quant_api import Int8WeightOnlyConfig, quantize_

pipeline = FluxPipeline.from_pretrained(...).to('cuda')
quantize_(pipeline.transformer, Int8WeightOnlyConfig()) # Or any other component(s)

@spaces.GPU
def generate(prompt: str):
    return pipeline(prompt).images[0]
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cbensimonย 
posted an update about 1 month ago
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5818
๐Ÿš€ ZeroGPU medium size is now available as a power-user feature

Nothing too fancy for nowโ€”ZeroGPU Spaces still default to large (70GB VRAM)โ€”but this paves the way for:
- ๐Ÿ’ฐ size-based quotas / pricing (medium will offer significantly more usage than large)
- ๐Ÿฆฃ the upcoming xlarge size (141GB VRAM)

You can as of now control GPU size via a Space variable. Accepted values:
- auto (future default)
- medium
- large (current default)

The auto mode checks total CUDA tensor size during startup:
- More than 30GB โ†’ large
- Otherwise โ†’ medium
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cbensimonย 
posted an update 10 months ago
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Hello everybody,

We've rolled out a major update to ZeroGPU! All the Spaces are now running on it.

Major improvements:

1. GPU cold starts about twice as fast!
2. RAM usage reduced by two-thirds, allowing more effective resource usage, meaning more GPUs for the community!
3. ZeroGPU initializations (coldstarts) can now be tracked and displayed (use progress=gr.Progress(track_tqdm=True))
4. Improved compatibility and PyTorch integration, increasing ZeroGPU compatible spaces without requiring any modifications!

Feel free to answer in the post if you have any questions

๐Ÿค— Best regards,
Charles
cbensimonย 
updated a Space almost 4 years ago