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Two controls are easy to confuse: a request-count limit caps how many requests can participate in one iteration, while a token-capacity limit caps the tokens represented by that iteration. Either can become the bottleneck. A useful benchmark therefore reports tokens per second and requests per second together with TTFT, end-to-end latency, inter-token latency, memory headroom, and the workload conditions that produced them.
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What “batch size” means in LLM inference
In ordinary machine-learning discussions, batch size usually means the number of examples processed together. LLM serving adds a second constraint because requests have different prompt lengths and are at different points in generation. A practical serving configuration normally exposes at least two limits:
- Maximum runtime batch size: the maximum number of requests allowed in one scheduled batch or iteration.
- Maximum runtime tokens: the token budget for the batched iteration. It limits the total token work represented at that point.
NVIDIA’s TensorRT-LLM tuning guidance illustrates why both settings matter. A request-count limit can bind even when token capacity remains available: three thousand short requests may hit a batch-size ceiling before they consume the configured token budget, while fewer long requests can exhaust token capacity first. The exact definition of “runtime tokens” is engine-specific. In the TensorRT-LLM example, the count includes summed context tokens and one token for each generation request in that iteration; do not apply that accounting rule to another engine without checking its documentation.
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| Control | What it limits | When it binds first | What to inspect |
|---|---|---|---|
| Request-count limit | Number of active requests in an iteration | Many short prompts or many concurrent users | Active-request count, scheduler queue, unused token budget |
| Token-capacity limit | Token work admitted to an iteration | Long prompts, long generations, or mixed-length traffic | Prompt and generated-token lengths, KV-cache use, remaining token budget |
Increasing one limit does not help when the other is the binding constraint. Treat the pair as workload controls, not as a universal performance rating for a model or GPU.
A published configuration is an illustration, not a target
NVIDIA’s TensorRT-LLM performance-tuning article shows a configuration for Llama-3.1-8B-Instruct-FP8 with a maximum runtime batch size of 3,840 and maximum runtime tokens of 7,680. In that article’s particular benchmark setup, it reports 11,076.7700 output tokens per second per GPU and 162.6706 milliseconds of average TTFT. Those figures belong to the specified model, precision, engine, hardware, workload, and measurement method. They are not a generally recommended batch size or an expected result for every deployment.
Choose the serving objective before changing a limit
“Maximize throughput” can mean several different things. A batch setting that raises system-wide output tokens per second may make each user wait longer for the first token or receive generated tokens more slowly. Decide which outcome matters for your service.
| Objective | Primary metric | Typical trade-off | Useful workload |
|---|---|---|---|
| Highest aggregate capacity | Output tokens per second and requests per second | Higher queueing and per-request latency may be acceptable | Offline jobs, large document processing, or tightly controlled high load |
| Fast individual generation | Inter-token latency or tokens per user | Lower utilization can leave capacity unused | Interactive sessions where a smooth stream matters |
| Fast first response | TTFT and its tail percentiles | Smaller or more conservative batches may reduce batching efficiency | Chat, autocomplete, and agent actions that start work immediately |
| Balanced online service | TTFT, end-to-end latency, inter-token latency, and aggregate throughput together | Requires an explicit service-level target and admission policy | Mixed interactive traffic with a defined latency budget |
Write the target down before benchmarking. For example, a team might require a TTFT ceiling, an acceptable inter-token delay, and a minimum aggregate token rate at a stated arrival rate. Without those conditions, “best batch size” only means “fastest under whichever test happened to be run.”
Why batching improves utilization—and why larger is not always better
LLM generation has a prompt-processing phase (prefill) and an autoregressive decoding phase. During prefill, the engine processes a request’s context; during decoding, it repeatedly produces one or more next tokens while retaining attention state in the key-value (KV) cache. Combining work from several requests can keep accelerator compute busy and spread fixed overhead across more output tokens.
Large batches also increase resource pressure. Longer prompts consume more memory for the KV cache, and requests finish at different times. A scheduler that waits for a fixed batch to fill can introduce queueing, while a scheduler that admits work continuously must manage changing sequence lengths and memory fragments. Once memory, scheduling, or communication becomes the bottleneck, raising a limit can reduce useful throughput or violate latency goals.
Continuous batching for online traffic
Continuous batching allows new requests to join work while existing requests are still decoding, instead of waiting for every request in a fixed batch to finish. vLLM documents continuous batching and PagedAttention as serving capabilities intended to improve utilization and reduce memory waste. These features can be valuable for variable-duration online requests, but their presence does not prove that one engine is fastest for every model, accelerator, precision, or traffic shape. Measure the implementation you plan to run.
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Build a benchmark that represents production
Benchmark results are only meaningful when the tested workload resembles the service you intend to operate. Before changing batch limits, record the following characteristics.
- Model and serving implementation: exact model revision, quantization or precision, runtime, kernel or engine build, and hardware.
- Prompt lengths: use a distribution, not just one convenient prompt. Include the long-context cases that consume the most KV-cache memory.
- Output lengths: record requested and realized generated-token distributions. Early stopping, stop sequences, and maximum-output settings change the work.
- Traffic control: state either request arrival rate, concurrency, or both. A closed test that keeps a fixed number of clients behaves differently from an open test that sends requests at a fixed rate.
- Generation settings: sampling, beam behavior if used, maximum new tokens, stop conditions, and streaming mode.
- Measurement boundary: specify whether timing starts at client submission, server admission, or model execution, and whether queueing, network, preprocessing, and postprocessing are included.
NVIDIA’s benchmarking guidance emphasizes that concurrency, request rate, maximum batch size, and other parameters directly affect measured throughput and latency. Report them beside every result; otherwise two apparently conflicting numbers may simply describe different workloads.
Use a workload matrix, not one test point
Create several representative cells, such as short prompts with short outputs, long prompts with short outputs, short prompts with long outputs, and mixed traffic. For each cell, test the arrival rates or concurrency levels that matter operationally. Keep the model, hardware, precision, and software build unchanged while sweeping only the batch or token limits you are evaluating.
Warm the engine before collecting measurements, discard startup effects, and run long enough to capture queue buildup and request completions. If the service has autoscaling or admission control, test the policy as deployed rather than measuring an unconstrained single process and assuming the result will hold at fleet level.
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Measure throughput and latency together
A single tokens-per-second number cannot tell you whether users are receiving acceptable service. Use a metric set that separates system capacity from individual experience.
| Metric | What it answers | Important qualification |
|---|---|---|
| Output tokens per second | How much generated text the system produces in aggregate | State whether the figure is per GPU, per replica, or for the whole service, and define which tokens count. |
| Requests per second | How many requests complete over time | Meaning depends on output-length distribution; pair it with token throughput. |
| TTFT | How long a user waits for the first streamed token | NVIDIA defines it as including queuing, prefill, and network latency. Longer prompts generally increase KV-cache computation. |
| End-to-end latency | How long the complete request takes | Include queueing and service overhead relevant to the user; report a distribution or percentiles when available. |
| Inter-token latency | How quickly successive tokens arrive after the first | For long outputs, tokens per user approaches the inverse of inter-token latency. |
| Memory headroom | How close the configuration is to an out-of-memory condition | Track peak allocation and KV-cache occupancy, not just the nominal batch setting. |
System-level token throughput and per-user speed answer different questions. A highly loaded server can produce many tokens per second overall while each individual stream advances slowly. Conversely, a low-concurrency test may give excellent per-user speed but understate the capacity available when requests overlap.
Report tails, not only averages
Average TTFT or average end-to-end latency can hide a queueing problem. At minimum, inspect the latency distribution and any tail percentile your service-level objective uses. A batch limit that improves average throughput but creates occasional very long waits may be unacceptable for interactive traffic. If your benchmark does not provide tail measurements, label that limitation rather than implying that the average describes every request.
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Separate maximum-throughput and low-latency tests
NVIDIA’s TensorRT-LLM benchmarking documentation describes throughput mode as testing an engine or PyTorch backend “under maximum load to provide an upper bound throughput number.” That mode is useful for finding the capacity ceiling of the tested setup. It does not demonstrate that the service meets a latency target under realistic traffic.
Run a distinct low-latency test at the arrival rate or concurrency you expect for users. Keep the same prompt and output distributions, then compare TTFT, end-to-end latency, inter-token latency, and completed throughput. You need both views:
- Maximum-load test: answers how much work the configuration can push when demand is intentionally saturated.
- Latency-target test: answers whether users receive responses within the required time while the system operates at a specified load.
Do not label the first result “production throughput” unless production uses the same load, measurement boundary, and workload assumptions.
A practical tuning procedure
- Define service targets. Set the required aggregate output-token rate or request rate, TTFT target, acceptable completion latency, and inter-token experience. Identify whether the workload is offline, interactive, or mixed.
- Capture real length distributions. Measure prompt tokens and generated tokens separately. Keep the percentile or bucket information needed to reproduce long and short cases.
- Choose an initial safe configuration. Start below the memory limit with conservative request and token caps. Verify that the model loads, the KV cache has headroom, and no scheduler errors occur.
- Identify the binding limit. During a run, inspect active requests, admitted tokens, queue depth, and memory. If requests reach the count cap while token capacity is unused, the request limit is binding. If token capacity or memory fills first, the token limit is binding.
- Sweep one control at a time. Increase the binding limit in measured steps while holding workload, software, and hardware constant. Re-run both saturated and latency-target tests after each change.
- Stop at the first unacceptable constraint. Stop increasing when memory headroom becomes unsafe, TTFT or tail latency exceeds the target, inter-token latency degrades, or aggregate throughput stops improving. The best setting is the highest one that still satisfies the stated objective, not necessarily the largest accepted value.
- Validate mixed traffic. Test the short and long request classes together. A limit that looks optimal for uniform prompts can behave poorly when long contexts compete with short interactive requests.
- Repeat after software changes. Runtime versions, kernels, quantization, scheduler policies, and hardware affect the result. Treat a batch limit as a deployment parameter tied to a specific stack, not as a permanent model property.
Diagnose common bottlenecks
Request cap reached while token budget remains
This pattern indicates that the request-count limit is binding. It often appears with many short prompts or high concurrency. Raise the request limit only if memory and latency measurements show enough headroom; otherwise, improve admission control or use a scheduler policy that avoids unnecessary queueing.
Token capacity or KV-cache memory fills first
Long prompts and long generations can exhaust token capacity even when the number of requests is modest. Lower the token cap, reduce maximum context or output settings where product requirements allow, increase available memory, or separate long-context traffic from latency-sensitive requests. Do not infer that a high request-count setting is safe merely because few requests are active.
Throughput rises but TTFT worsens
The larger batch is likely increasing queueing or prefill contention. Compare TTFT with the same arrival rate and prompt distribution, and inspect the time from client submission through admission. A smaller cap, an arrival-rate limit, or a separate queue for interactive traffic may better satisfy the service target.
Average latency looks acceptable but users see stalls
Inspect tail latency and inter-token latency. Uneven sequence lengths, memory pressure, or scheduler contention can affect a subset of requests without moving the average much. Test mixed-length traffic and report the relevant tail percentile.
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Core-model timing is much lower than observed latency
The measurement boundaries differ. NVIDIA’s Triton TensorRT-LLM backend documentation notes that core-model latency excludes preprocessing and postprocessing that may be performed by a third-party library. User-facing timing should include the request path that matters: queueing, preprocessing, model execution, postprocessing, streaming, and network effects where applicable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare serving engines and configurations fairly
TensorRT-LLM and vLLM are examples of serving software, not a universal ranking. To compare two engines or two batching policies, hold constant:
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- Accelerator model, count, clock or power policy, host CPU, and memory configuration.
- Prompt and output length distributions, stop behavior, and streaming protocol.
- Arrival rate or concurrency, warm-up procedure, test duration, and retry policy.
- Batch and token limits being tested.
- Measurement boundaries, including queueing, preprocessing, postprocessing, and network time.
Then compare aggregate output tokens per second, requests per second, TTFT, end-to-end latency and tails, inter-token latency, memory headroom, and which limit binds. A result from an offline maximum-load run should not be presented as evidence about interactive latency, and a core-model timing should not be compared directly with an end-to-end client measurement.
Document each result so it can be reproduced
A concise benchmark record prevents misleading “best batch size” claims. Include:
- Model name and revision, precision, runtime and engine versions, and hardware.
- Maximum runtime batch size and maximum runtime tokens, plus any scheduler or continuous-batching settings.
- Prompt-token and output-token distributions, including maxima and representative percentiles.
- Concurrency or request arrival rate, test duration, warm-up handling, and generation settings.
- Output tokens per second, requests per second, TTFT, end-to-end latency, inter-token latency, and tail statistics where available.
- Peak memory use, observed KV-cache occupancy, and the binding constraint.
- Whether the run was maximum-load throughput, low-latency, or a production-like mixed test.
- What the clock includes and excludes, especially preprocessing, postprocessing, queueing, and network time.
Keep the raw configuration with the result. If a runtime interprets token capacity differently, record that definition as well; otherwise a future operator may reproduce the numeric setting but not the behavior.
Decision framework: selecting a production limit
Use the following order when several configurations appear viable:
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- Discard any configuration that misses the TTFT, completion-latency, or inter-token target at the intended load.
- Among the remaining configurations, choose the one with the highest useful aggregate throughput for the actual request mix.
- If two settings are close, prefer the one with more memory and latency headroom, because production traffic varies and software updates can shift the boundary.
- Recheck the choice when traffic changes from closed concurrency to open arrival rate, when prompt lengths grow, or when a new runtime or hardware generation is deployed.
This process turns batch size into an operational control. It also makes trade-offs visible: a setting optimized for offline capacity may be different from one chosen for a chat service, even on the same model and GPU.
Frequently asked questions
Is a larger batch always more efficient?
No. Larger batches can improve utilization until memory pressure, queueing, scheduling overhead, or latency objectives become limiting. Measure the point at which useful throughput stops improving for your workload.
Should I tune request count or token capacity first?
Measure which one binds under representative traffic. Raise the binding limit cautiously; changing the non-binding limit will not address the bottleneck and can make capacity planning harder.
Can I compare a tokens-per-second result from two different benchmarks?
Only if model, hardware, precision, lengths, traffic control, batch limits, and measurement boundaries match. Otherwise the figures describe different experiments.
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Why do long prompts affect first-token latency?
TTFT includes queueing and prefill, and longer prompts require more KV-cache computation during prefill. A workload with the same request count but longer contexts can therefore have materially different TTFT.
What should I do when an engine’s token counter is unclear?
Read that engine’s runtime documentation and record its accounting rule in the benchmark report. Do not assume that a token-capacity number has the same meaning across serving systems.
Bottom line
Choose batch limits from measured workload behavior, not from a standalone number. Distinguish request-count and token-capacity ceilings, benchmark both maximum-load throughput and latency-target operation, and report TTFT, end-to-end latency, inter-token latency, throughput, memory headroom, and measurement boundaries together. The winning configuration is the one that meets your users’ latency requirements while delivering the most useful aggregate capacity on the exact stack you will run.
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