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Load Balancing and Scaling LLM Serving
Load balancing and scaling LLM serving involves distributing inference requests across multiple model instances or GPUs to prevent bottlenecks and ensure consistent response times under varying traffi
Load balancing and scaling LLM serving involves distributing inference requests across multiple model instances or GPUs to prevent bottlenecks and ensure consistent response times under varying traffic loads. DigitalOcean's coverage likely addresses strategies such as horizontal scaling, request queuing, and routing techniques tailored to the unique computational demands of large language models. The content probably includes practical guidance on deploying scalable LLM infrastructure using cloud resources, covering trade-offs between cost, latency, and throughput.
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Source: DigitalOcean | 2026-04-15