📦

Batching Strategies

Maximize throughput with intelligent request batching

Smart batching is key to maximizing GPU utilization in production LLM deployments. Continuous batching dynamically adds and removes requests mid-generation, eliminating idle time. Chunked prefill prevents long prompts from blocking other requests. These techniques can increase throughput by 10-20x compared to naive static batching.

3 Techniques

Continuous Batching

Dynamic batching that adds/removes requests as they complete. Maximizes GPU utilization.

Speed:10-20x throughput vs static batching

NVIDIAAMD

Speculative Decoding

Use small draft model to predict tokens, verify with large model. 2-3x faster with identical outputs.

Speed:2-3x for autoregressive generation

NVIDIAAMD

Chunked Prefill

Split long prompts into chunks and interleave with decode steps. Reduces latency for short requests in mixed workloads.

Speed:2-5x latency reduction for short requests

NVIDIAAMD