In the world of LLM serving, the most critical configuration is often the batching strategy. It is the primary lever for balancing throughput (how many requests you can handle) against latency (how fast each request feels).
Static Batching vs. Continuous Batching
Static Batching
The traditional approach waits for a fixed number of requests to arrive before processing them together. This maximizes GPU utilization but introduces significant "wait time" for the first request in the batch, leading to variable inference latency.
Continuous Batching (vLLM)
Modern runtimes like vLLM use continuous batching (or iteration-level scheduling). Instead of waiting for the whole batch to finish, new requests can be inserted as soon as any sequence in the current batch completes. This is essential for high-concurrency SaaS applications.
Finding the Sweet Spot
Your batching strategy must align with your system SLOs. For interactive chat, you might sacrifice some throughput for lower TTFT. For batch model scoring, you'll push for maximum batch size.
Final Takeaway
Batching is not a "set and forget" configuration. It requires continuous tuning based on your request patterns, model size, and hardware. By moving to continuous batching, most teams can significantly improve both their GPU utilization and their user experience.
Need help tuning your LLM serving stack for better throughput or lower latency? We help teams optimize vLLM, Triton, and other runtimes for production workloads. Book a free infrastructure audit and we’ll review your batching and serving configuration.
