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Published Oct 3, 2023

Generative models: exploration to deployment

Chris Benson and Daniel Whitenack delve into optimizing generative AI models by discussing hardware considerations, open-source tools, and infrastructure security, while also emphasizing the importance of experimentation and staying updated with infrastructure trends for effective model deployment.
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  • Deployment Options

    Exploring deployment options for AI models reveals a variety of methods, from cloud-based platforms to local infrastructure. emphasizes the importance of understanding these options, suggesting that developers experiment with different models to build intuition before committing to a specific model family 1. He recommends deploying models as REST APIs, which allows for separation of concerns between model serving and application code 2. This approach can be implemented on various infrastructures, including serverless environments and containerized model servers 3.

    Even if you end up using a model that's behind some API that's hosted by someone else, it can be useful and instructive in building your own intuition even to just try deploying one of these models.

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    Understanding these deployment strategies is crucial for efficient AI model integration.

       

    Infrastructure Trends

    Recent trends in AI infrastructure are shaping how models are deployed, with a notable shift towards serverless environments. highlights Cloudflare's entry into the serverless GPU space, indicating a growing trend among major players to support serverless GPU workloads 4. This development reflects the rapid evolution of AI infrastructure, where new models and tools are constantly emerging.

    Cloudflare announced their workers AI, and I think this is the latest in this sort of series of serverless GPU solutions.

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    As these trends continue, developers must stay informed to effectively manage and deploy AI workloads.

       

    Streamlined Deployment

    Streamlining model deployment involves strategic decisions, particularly when considering single-instance setups. advises starting with a single processor or accelerator to simplify the process before scaling up 5. He also points to Hugging Face as a valuable resource for finding open access models, likening it to GitHub for AI models 6.

    I personally recommend to people, even if it's a bit larger of a model, convince yourself that you can't run it on a single accelerator or a single cpu before you make the jump to spin up a gpu cluster.

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    These strategies can significantly ease the initial steps in deploying AI models.

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