AI Latency vs Batch Size Calculator

What Is the AI Latency vs Batch Size Calculator?

The AI Latency vs Batch Size Calculator works out total request latency and system throughput at a given batch size, and separately, the largest batch size that keeps latency within a target service-level objective (SLO). As the Anyscale documentation on LLM latency and throughput metrics explains, batch size, the number of requests processed together on a GPU at once, is the central quantity governing the tradeoff between how fast any single request completes and how many requests a system can serve per second overall.

Figure out which mode answers your question: latency and throughput mode converts a benchmarked TTFT and inter-token latency at one batch size into concrete request-level and system-level numbers, while max batch size for SLO mode works the relationship in reverse, finding the largest batch size that still meets a maximum acceptable latency target.

TTFT, TPOT, and Total Request Latency

Total request latency breaks into two genuinely different components: time to first token (TTFT), the delay before any output appears, and inter-token latency (TPOT, time per output token), the pace of token generation after that. Total latency works out to TTFT plus output tokens multiplied by TPOT, so a request generating a short 50-token reply is far less sensitive to a TPOT increase than one generating a 2,000-token long-form response, even at the exact same batch size and hardware configuration. According to Databricks' guidance on LLM inference performance engineering, TTFT is typically compute-bound and dominated by prompt processing, while the subsequent token-by-token decoding phase is typically memory-bound, meaning the two components respond differently to batch size changes and should be reasoned about separately rather than as one blended figure. What is more, a serving system optimized purely for a fast average TTFT can still deliver a poor overall experience if TPOT degrades badly under load, since users notice a slow stream of tokens just as readily as a slow start. Come back to both figures together whenever evaluating a serving configuration change, rather than treating either one as a standalone success metric on its own.

Why Batch Size Trades Latency for Throughput

System throughput works out to batch size divided by TPOT, so packing more requests into a batch increases total tokens served per second even as each individual request's TPOT grows somewhat with batch size. Come back to this relationship whenever tuning a serving configuration, since Anyscale's own benchmarking of continuous batching documented up to a 23x throughput improvement over naive static batching, achieved specifically by keeping GPU utilization high without forcing every request in a batch to wait for the slowest one to finish.

Finding the Maximum Batch Size Within a Latency SLO

Work out the largest workable batch size by benchmarking TPOT at two batch sizes, a small reference and a larger one, then interpolating between them to estimate TPOT at any batch size in between. Given that real TPOT-vs-batch curves are typically sublinear at first and steepen sharply once a GPU's memory bandwidth saturates, a linear interpolation between two benchmarked points is a reasonable working approximation only within that measured range, not a reliable extrapolation far beyond it. Pull out the specific batch size where projected total latency first exceeds your SLO, since that is the practical ceiling for how aggressively a serving system can batch requests before violating a latency commitment.

Set out separate SLO calculations for genuinely different request types where your workload mixes them, since a chat completion and a long-document summarization request have very different output token counts and therefore very different sensitivity to the same TPOT increase, in line with how the LLM Inference Handbook's treatment of static, dynamic, and continuous batching frames batching strategy as inherently workload-dependent rather than a single universal configuration. A single shared batch size configuration tuned only for the shorter, more common request type can silently violate the SLO for the rarer, longer request type, exactly the kind of gap a per-type calculation is built to catch before it reaches production and starts affecting real users on the affected request path.

Accuracy and Limitations

The arithmetic here is exact given accurate TTFT, TPOT, and batch size inputs. This calculator does not model a specific GPU's or framework's actual TPOT-vs-batch-size curve, since that depends on hardware generation, model architecture, quantization, and serving framework in ways too specific to generalize into one formula; always benchmark your own actual deployment on your own actual hardware rather than borrowing a curve from an unrelated setup, in line with infrastructure sizing guidance for LLM inference deployments, which treats hardware-specific benchmarking as a prerequisite step rather than an optional nice-to-have before any serious capacity planning. The SLO mode's linear interpolation is explicitly a simplification, most reliable for batch sizes between the two benchmarked reference points and progressively less reliable the further a projection extends beyond them.

The Most Common Batch Size Tuning Mistake

The mistake I see most often is tuning batch size purely for maximum throughput without checking the resulting per-request latency against any actual SLO or user-facing requirement, discovering only in production that responses have become unacceptably slow for real users. With that in mind, always calculate the specific latency impact of a proposed batch size increase before deploying it, rather than assuming "more batching is always better" without a latency check attached. This turns up most often when a team benchmarks throughput in isolation during load testing, without simultaneously tracking the per-request latency that real users would actually experience at that same configuration, exactly the gap the same inference performance engineering guidance cited earlier warns against by recommending both metrics be tracked together, never one without the other. Once a workable batch size is found, our LLM Token Cost Calculator and AI API Call Budget Planner help translate that serving configuration into cost and capacity planning.