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There is no universally best LLM batch size. Choose the largest request batch or token budget that meets your service’s first-token, generation-speed, and memory targets for the traffic you actually expect. A setting that maximizes offline tokens per second can produce unacceptable queueing and time to first token (TTFT) for an interactive application.
Tune two separate constraints—how many requests may run together and how many tokens the iteration may hold—then measure aggregate throughput and user-facing latency on representative prompt and output lengths. The practical winner is the configuration that satisfies your latency SLO with the highest useful throughput, not the configuration with the biggest batch number.
What “batch size” means in an LLM server
In inference, a batch is the group of requests processed together during a runtime iteration. Batching lets a GPU perform work for multiple sequences in parallel, improving utilization when enough requests are available. However, every additional sequence consumes memory for activations and key-value (KV) cache, increases scheduling pressure, and can make a waiting request take longer to begin.
Serving systems expose at least two limits that are often both called batch size:
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| Control | What it limits | Why it matters |
|---|---|---|
| Maximum runtime batch size | The maximum number of requests in one iteration or batch. | It can stop the scheduler from admitting another request even when token memory remains. |
| Maximum runtime tokens | The token budget available to the batched iteration. | Long prompts or many active generations can exhaust capacity before the request-count limit is reached. |
NVIDIA’s TensorRT-LLM tuning guidance demonstrates that the request-count cap can bind while the token cap still has room. The exact definition of “runtime tokens” is engine-specific. In the TensorRT-LLM example, the count includes summed context tokens plus one token for each generation request in that iteration; do not apply that accounting rule to another runtime without checking its documentation.
Think of the scheduler as enforcing both ceilings at once. A candidate batch is admissible only when it satisfies the request limit, the token limit, available KV-cache memory, and any engine-specific constraints. Raising one ceiling does not help if another is the binding limit.
Start with the serving objective
Before changing a flag, decide which outcome you are optimizing. NVIDIA’s tuning material frames three different goals:
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Offline jobs, bulk summarization, and large text-processing queues generally value total output tokens per second or requests per second. They can tolerate more queueing because no person is waiting for each token. A high-concurrency, maximum-load test is appropriate for finding an upper bound for the tested setup.
Fast per-user generation
For an interactive answer, users perceive inter-token latency—the time between generated tokens—as the streaming speed. More batching can increase total GPU utilization while adding contention that slows each individual stream. Optimize tokens per user and inter-token latency, not only the system-wide sum.
Quick first-token response
Chat and autocomplete workloads are sensitive to TTFT. TTFT includes time spent waiting in a queue, prompt prefill, and network latency. A very large batch may improve eventual throughput while delaying admission or prefill for a new request. Set a TTFT target first, then find the highest throughput that stays below it.
These objectives can conflict. Record the priority explicitly—for example, “p95 TTFT under 300 ms and p95 end-to-end latency under 8 seconds, while maximizing output tokens per second.” Without a target, “best batch size” has no objective definition.
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Batching behavior depends on the distribution of traffic, not just an average request. Capture the following before tuning:
- Prompt-length distribution, including median and high-percentile context lengths.
- Output-length distribution or a fixed generation limit used by the application.
- Request arrival rate, concurrency, or both. A batch cannot fill if requests arrive slowly.
- Generation settings such as maximum new tokens, sampling, beam search, and stop conditions.
- Model architecture, quantization or precision, accelerator type, and serving engine version.
- Whether requests stream tokens and whether cancellation is common.
Longer prompts increase KV-cache computation during prefill. A workload of short prompts with short answers may support a much larger request count than a workload with long contexts and long generations, even on the same GPU. Conversely, a low arrival rate may make a nominally large maximum batch irrelevant because the scheduler rarely has enough work at once.
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A repeatable batch-size tuning procedure
1. Define the service-level targets
Write down limits for TTFT, inter-token latency, end-to-end latency, error rate, and memory utilization. Specify whether the limit is an average or a percentile such as p95 or p99. Also state the desired aggregate metric: output tokens per second, requests per second, or completed jobs per hour.
2. Build a representative workload
Use production-like prompt and output lengths rather than a single convenient example. Preserve the observed mix of short, medium, and long requests. If production traffic is not available, create separate scenarios for short interactive prompts, long-context prompts, and long generations; report each scenario separately instead of averaging unlike workloads.
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3. Fix the software and hardware configuration
Keep the model revision, tokenizer, precision, tensor and pipeline parallelism, GPU type, driver, runtime, and generation parameters constant while sweeping batch settings. A change in any of these can move the bottleneck and invalidate a comparison.
4. Establish safe memory limits
Begin with conservative request and token caps. Observe allocated memory, KV-cache usage, allocator fragmentation, and out-of-memory events during a sustained run. Leave headroom for variability and service overhead; a configuration that barely fits a synthetic batch may fail when a longer-than-average prompt arrives.
5. Sweep one constraint at a time
Test a grid of maximum runtime batch sizes and maximum runtime-token values. For each pair, run long enough to reach steady state and repeat the run if results are noisy. First identify which limit binds: request count, tokens, KV cache, compute, or an external queue.
6. Test at several load levels
Use at least a low-load interactive test, a target-load test, and a saturation test. Low load reveals responsiveness when requests are sparse. Target load shows whether the service meets its SLO under expected traffic. Saturation reveals the upper-bound throughput, but it should not be presented as production performance.
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Plot throughput against TTFT, inter-token latency, and end-to-end latency. Throughput often improves as the batch grows, then flattens while latency rises sharply. Choose a point near that knee that still meets the tail-latency and memory targets. If no point meets the targets, reduce model cost, add capacity, change scheduling, or revise the SLO; do not simply keep increasing the batch.
Metrics that must be measured together
| Metric | What it answers | Important qualification |
|---|---|---|
| Time to first token (TTFT) | How quickly a request starts streaming. | Includes queueing, prefill, and network latency; report a percentile for online services. |
| End-to-end latency | How long the complete request takes. | Depends on output length and includes service overhead; compare like-for-like lengths. |
| Inter-token latency | How frequently tokens arrive after the first token. | It is a direct indicator of streaming smoothness and per-user speed. |
| Output tokens per second | How much generated text the system produces in aggregate. | State whether the value is per GPU, per replica, or for the whole service. |
| Requests per second | How many requests complete over time. | Meaningful only with a defined prompt/output distribution and success criteria. |
| Tokens per user | How much generation speed an individual request receives. | System throughput can rise while per-user speed falls as contention increases. |
For long outputs, tokens per user approaches the inverse of inter-token latency. This relationship makes it possible for aggregate output tokens per second to rise while each user sees slower streaming. Report both views.
Throughput mode versus latency mode
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 capacity planning and comparing a fixed setup with another fixed setup. It does not demonstrate that the service meets an interactive latency target under real traffic.
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Continuous batching and dynamic scheduling
Static batching waits for a fixed group of requests, runs them, and then starts another group. This can leave the GPU idle when requests finish at different times or when arrivals are uneven. Continuous batching allows new requests to join scheduling iterations while other sequences are still generating.
vLLM documents continuous batching and PagedAttention as features intended to improve utilization and reduce memory waste. These capabilities are particularly relevant to online serving with variable-length requests. They are not proof that one engine is fastest for every model, accelerator, or traffic pattern; measure the engine with your workload.
When continuous batching helps
- Requests have widely varying output lengths, so static batches would wait for the longest sequence.
- Arrivals are asynchronous and a steady stream of work is available.
- KV-cache allocation is a dominant memory concern.
What to verify
- How the engine defines a scheduling iteration and counts tokens.
- Whether a batching window or admission delay is configurable.
- How prefill work competes with decode work.
- How cancellations, timeouts, and long-running sequences release memory.
Diagnose the binding limit
Request-count bound
If active requests consistently equal the maximum runtime batch size while token capacity remains unused, the request cap is binding. Raising the token limit alone will not increase concurrency. Increase the request cap only after confirming that memory and latency remain safe.
Token-capacity bound
If the token budget is exhausted with fewer requests than the request-count maximum, long prompts or generations are consuming capacity. Raising the request cap will have no effect. Test a higher token budget only if KV-cache and latency headroom are available.
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Memory bound
Out-of-memory errors, allocator failures, or rapidly shrinking free memory indicate that the practical limit is below the configured maximum. Reduce token capacity, lower concurrency, shorten retained context, or use a memory-saving precision or cache strategy supported by the engine.
Compute bound
If GPU utilization and kernel time remain high while memory has headroom, more batching may improve aggregate throughput until the processor is saturated. Watch TTFT and inter-token latency for the point where additional work harms the user experience.
Arrival-rate bound
If the system is fast but batches remain small at the intended load, there may not be enough simultaneous requests to benefit from a larger maximum. Increasing the limit will not create work. Optimize request routing, replication, or admission policy instead.
Interpreting a concrete TensorRT-LLM example
NVIDIA’s tuning article gives 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. The same article reports 11,076.7700 output tokens per second per GPU and 162.6706 ms average TTFT for its benchmark configuration.
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These figures are illustrative measurements from that particular model, precision, hardware, engine, workload, and benchmark procedure. They are not a recommended batch size or expected speed for every Llama deployment. Reproduce the relevant conditions before using them for capacity planning, and do not compare them with another result unless the model, hardware, lengths, arrival pattern, concurrency, precision, and measurement boundaries match.
Compare serving engines fairly
TensorRT-LLM and vLLM are examples of serving software, not an apples-to-apples ranking. A defensible comparison uses the same:
- Model files, tokenizer, revision, and numerical precision.
- GPU model, number of GPUs, interconnect, driver, and power settings.
- Prompt and output length distributions.
- Arrival rate or concurrency, warm-up procedure, and test duration.
- Sampling, stopping, streaming, and cancellation behavior.
- Maximum request and token limits, queueing policy, and batching window.
- Measurement boundary, including queueing, preprocessing, postprocessing, and network time.
Publish raw configuration alongside every result. A single tokens-per-second number without these conditions cannot establish superiority.
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Core-model timing is narrower than service timing. NVIDIA’s Triton TensorRT-LLM backend documentation notes that its core-model latency excludes preprocessing and postprocessing that may be handled by a third-party library. Tokenization, request validation, routing, queueing, detokenization, serialization, and network transfer can all affect TTFT or total latency.
Instrument timestamps at ingress, queue admission, prefill start, first-token emission, each subsequent token, and completion. Report both engine timing and end-to-end timing when diagnosing a regression. Use the end-to-end value for user-facing SLOs.
A practical benchmark record
For every run, save a machine-readable record containing:
- Model and revision, precision, engine and version, hardware, and parallelism.
- Maximum runtime batch size and maximum runtime tokens.
- Prompt and output length statistics, including percentiles.
- Arrival rate or concurrency, warm-up duration, test duration, and number of completed requests.
- TTFT, inter-token latency, end-to-end latency, and tail percentiles.
- Aggregate output tokens per second and requests per second.
- Peak memory, KV-cache occupancy, errors, timeouts, and cancellations.
- Whether queueing, preprocessing, postprocessing, and network effects are included.
This record makes a future regression diagnosable. If throughput changes, you can determine whether the cause was workload mix, software, hardware, a binding limit, or a changed measurement boundary.
Common mistakes and their fixes
Using a universal batch recommendation
Problem: A value copied from another model or GPU is treated as optimal.
Fix: Sweep request and token limits on the target workload and choose against explicit SLOs.
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Reporting only aggregate tokens per second
Problem: A high total hides slow first responses or poor per-user streaming.
Fix: Report TTFT, end-to-end latency, inter-token latency, requests per second, and tail percentiles alongside aggregate throughput.
Confusing maximum load with production load
Problem: An upper-bound throughput test is presented as a guarantee.
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Ignoring long-tail requests
Problem: A median-length benchmark fits comfortably but production p99 prompts exhaust token capacity.
Fix: Include high-percentile prompt and output lengths, or enforce explicit per-request limits and measure their effect.
Measuring only model kernels
Problem: The reported latency omits tokenization, queues, postprocessing, or network time.
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Fix: Trace the complete request path and use end-to-end measurements for user promises.
Changing several variables at once
Problem: A new engine, precision, batch limit, and hardware configuration are compared in one run.
Fix: Hold all but one factor constant during a sweep, then run a controlled confirmation test.
Decision checklist
- Have you defined whether throughput, TTFT, per-user speed, or a combination is the priority?
- Are maximum request count and maximum token capacity configured and analyzed separately?
- Does the benchmark reproduce production prompt and output lengths?
- Are arrival rate or concurrency, generation settings, and batching policy recorded?
- Are average and tail latency reported together?
- Can you identify whether request count, tokens, memory, compute, or arrivals are binding?
- Does the timing include queueing, preprocessing, postprocessing, and network effects relevant to users?
- Have you tested both target load and maximum load?
- Is there enough memory headroom for variability and operational overhead?
- Are engine comparisons made with identical model, hardware, workload, and measurement boundaries?
The Bottom Line
Choose batch limits from measured workload behavior: sweep request count and token capacity, identify the binding constraint, and select the largest configuration that meets your TTFT, inter-token, end-to-end latency, and memory targets. Treat maximum-load throughput as an upper bound, and validate every decision with end-to-end tests that reflect real traffic.
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