Dynamic Batching: Balancing LLM Throughput and Latency
Dynamic batching groups LLM requests like a bus that leaves on a schedule or when full, whichever comes first. This improves throughput in inference servers by avoiding long waits. The footgun: all requests in a batch are still held hostage by the slowest one.
Dynamic batching groups LLM requests like a bus leaving when full or on a fixed schedule, whichever comes first. Instead of waiting for a fixed number of requests, it launches a batch after a timeout or when a size limit is hit. This strategy improves throughput and latency in inference servers by ensuring requests aren't delayed indefinitely. The footgun: like static batching, the entire batch is still held hostage by the slowest request, meaning faster-finishing requests wait unnecessarily and waste GPU resources.
Read the original → bentoml.com
- #llm
- #inference
- #optimization
- #batching
- #gpu
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