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The 1979 Design Choice Breaking AI Workloads

cerebrium.ai

21–22 of 22 posts

Re: The 1979 Design Choice Breaking AI Workloads

#21

The gzip compression of layers is actually optional in OCI images, but iirc not in legacy docker images. The two formats are not the same. On SSDs, the overhead for building an index for a tar is not that high, if we're primarily talking about large files (so the data/weights/cuda layers instead of system layers). The approach from the article is of course still faster, especially for running many minor variations of…

Yeah that’s fair. For weights specifically there often isn’t a huge dedupe win across versions since retraining tends to change most of them. That said, we generally don’t advocate including model weights in container images anyway. The main benefit for us is avoiding the need to pull the full image up front and only fetching the data actually touched during startup. On the latency side, reads happen over a local network with caching and prefetching, so the impact on request latency is typically minimal.

Re: The 1979 Design Choice Breaking AI Workloads

#22

Why does the model data need to be stored in the image? Download the model data on container startup using whatever method works best.

hey cosmotic, we're not really advocating for storing model weights in the container image. even the smaller nvidia images (like nvidia/cuda:13.1.1-cudnn-runtime-ubuntu24.04) are about 2Gb before adding any python deps and that is a problem. if you split the image into chunks and pull on-demand, your container will start much faster.

Just pre-install the NVIDIA layer on the filesystem instead of docker-pulling it for every single machine.
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