TLDR Tech / Dev
Reliable LLM Inference at Scale
To handle the complexities of multi-tenant LLM serving, Databricks built a specialized abstraction called "model units" that allows for the precise allocation and scaling of GPU resources based on the specific computational costs of different workloads. Integrating these units into load-balancing and autoscaling systems can decrease GPU costs by more than 80% while still making sure latency targets are met even during unpredictable traffic spikes.
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