request batching strategies

**Request batching strategies** is the **set of policies for grouping inference requests to balance throughput, latency, fairness, and memory constraints** - batching strategy is one of the highest-impact serving configuration choices. **What Is Request batching strategies?** - **Definition**: Methods for deciding batch size, admission timing, and request compatibility. - **Common Strategies**: Includes static batching, dynamic batching, continuous batching, and priority-aware batching. - **Constraint Inputs**: Uses context length, expected output length, SLA class, and hardware state. - **System Effect**: Directly influences queue delay, decode efficiency, and tail latency. **Why Request batching strategies Matters** - **Performance Tradeoffs**: Aggressive batching boosts throughput but can hurt interactive latency. - **SLA Compliance**: Different traffic classes need different batching policies. - **Memory Safety**: Batch composition affects KV usage and out-of-memory risk. - **Fairness**: Policy design prevents starvation of short or high-priority requests. - **Cost Efficiency**: Optimized batching improves accelerator utilization and serving economics. **How It Is Used in Practice** - **Traffic Segmentation**: Separate interactive and offline jobs into distinct batching lanes. - **Adaptive Controls**: Adjust batch limits dynamically based on current queue and latency metrics. - **Replay Testing**: Evaluate strategies with production-like traces before deployment. Request batching strategies is **a central control surface in inference platform engineering** - well-tuned batching policies are essential for stable, efficient, and fair serving.