22 lines
951 B
Markdown
22 lines
951 B
Markdown
---
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title: Optimizing performance and sustainability for ai
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weight: 5
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tags:
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- keynote
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- panel
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---
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{{% button href="https://youtu.be/VcMOr1DtTWM" style="warning" icon="video" %}}Watch talk on YouTube{{% /button %}}
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A panel discussion with moderation by Google and participants from Google, Alluxio, Ampere and CERN.
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It was pretty scripted with prepared (sponsor specific) slides for each question answered.
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## Takeaways
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* Deploying an ML should become the new deployment a web app
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* The hardware should be fully utilized -> Better resource sharing and scheduling
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* Smaller LLMs on CPU only is pretty cost-efficient
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* Better scheduling by splitting into storage + CPU (prepare) and GPU (run) nodes to create a just-in-time flow
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* Software acceleration is cool, but we should use more specialized hardware and models to run on CPUs
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* We should be flexible regarding hardware, multi-cluster workloads and hybrid (onprem, burst to cloud) workloads
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