Third-party cyber evaluations involving OpenAI models
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Cloud & Infrastructure
Google Cloud Blog·May 27·8 min read
A Guide to AI Cold Starts on Cloud Run
TL;DR
Developers deploying AI models on Google Cloud Run faced cold start latencies up to 20 seconds—delays while serverless infrastructure initializes before responding to requests. Google Cloud documented mechanics and strategies for managing these startup delays across multiple regions.
✦ Why It Matters
Engineers can now make data-driven decisions on serverless versus managed infrastructure for AI workloads based on cold start trade-offs.
Key Takeaways
- Use Google Cloud CLI for concurrent model file downloads.
- Optimize GPU memory usage to avoid performance degradation.
- Treat GPUs as fungible compute resources for better scalability.
- Focus on infrastructure patterns to minimize cold start latency.
How It Works
The cold start process involves four phases: provisioning the GPU, streaming the container image, initializing the inference engine, and loading model weights into GPU memory. Each phase has specific time requirements, and optimizing the final phase, where model weights are transferred, is crucial for reducing overall latency.