Production latency is not one number. Measure time to first token (TTFT), the pace of streamed tokens (inter-token latency or TPOT), and the time until the complete response separately. Then measure each at the user-facing boundary and at the model server, using production-like prompt lengths, output lengths, arrival rates, concurrency, and streaming behavior.
A reliable optimization loop is: define the user objective and tail percentile, benchmark a representative workload, inspect production distributions and request slices, identify one bottleneck, change one variable, and rerun the same workload while checking output quality, capacity, and cost. A model-core benchmark can show faster kernels without making the user experience faster if queues, preprocessing, network hops, or postprocessing dominate.
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What “latency” means for an LLM application
Different latency metrics answer different user questions. A chat user may care about how long the interface appears idle, whether streamed text arrives smoothly, or when the answer is complete. Report all three when the product depends on all three.
| Metric | Definition | What it reveals | Important qualification |
|---|---|---|---|
| Time to first token (TTFT) | Time from request submission until the first output token is received. | Initial wait before visible output. | Usually includes queueing, prompt prefill, and network time. Longer prompts can increase it because the prompt must be processed to build the KV cache. |
| Inter-token latency (ITL) | Elapsed time between successive generated tokens. | Whether streaming feels smooth and how quickly decode proceeds. | Collect individual intervals and their distribution; an average can hide pauses. |
| Time per output token (TPOT) | (request_latency - time_to_first_token) / (total_output_tokens - 1) |
Average generation time after the first token. | For an empty or one-token response, the denominator is zero or undefined; report the case separately rather than inventing a value. |
| Normalized time per output token (NTPOT) | request_latency / total_output_tokens |
End-to-end time normalized by response length. | Metric names and formulas differ between tools, so state the formula whenever you compare results. |
| End-to-end request latency | Time from request submission until the complete response is received. | Total wait for a finished answer. | Includes queueing, batching, network transfer, and application handling. In streaming systems, partial-result processing can add work before completion. |
| Throughput | Requests per second, output tokens per second, and, when useful, total input-plus-output tokens per second. | How much work the service completes at a given load. | Requests per second alone is misleading when context and response lengths vary. |
These definitions follow the terminology documented by Google Cloud and NVIDIA. Tool implementations are not interchangeable: a comparison is meaningful only when the boundaries and formulas match.
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Start with a user-visible objective
Do not begin by selecting a batch size or a faster accelerator. First decide which interaction the service must support.
- Interactive chat: minimize the high-percentile TTFT so the interface begins responding promptly, then control ITL so text does not arrive in long pauses.
- Streaming code or document generation: track TTFT and ITL separately; a good first token does not compensate for a slow decode stream.
- Non-streaming extraction or classification: end-to-end latency may matter more than token pacing because the client sees no partial answer.
- Batch jobs: maximize useful tokens per second subject to a completion deadline, rather than optimizing an interactive TTFT target.
Set the objective at an explicit percentile under an explicit load, such as a tail percentile at the expected concurrency and arrival rate. There is no universal acceptable TTFT or completion time. The right target depends on prompt and output lengths, model, protocol, streaming behavior, and the product’s interaction design. Do not copy a vendor’s example target as a general service-level objective.
Build a repeatable measurement loop
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Define the measurement contract
Write down the start and end events, the token-counting method, the percentile targets, and the load conditions. Decide whether the client timer starts when the request enters your SDK, when bytes leave the client, or at another precise event. For streaming, define “first token received” as the first decoded output token, not merely the first response header or byte.
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Create a representative workload
Use a stable prompt dataset or controlled input and output sequence lengths that reflect production. Include short and long contexts, short and long generations, system prompts, tool calls if applicable, and the actual request mix. Reproduce the expected arrival pattern and concurrency. A test made only of identical short prompts can make cache behavior and queueing look much better than they are.
The NVIDIA Triton TensorRT-LLM backend benchmark supports a dataset or user-specified input and output sequence lengths. Its arrival timing can be changed from the default exponential pattern to constant arrival timing. Choose deliberately: an exponential pattern approximates bursty random arrivals, while constant arrivals help isolate capacity at a controlled rate.
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Warm up and record conditions
Separate cold-start observations from steady-state observations. Record model and version, serving software and version, hardware, quantization, batch policy, cache state, replica count, warm-up duration, request protocol, and whether traffic is encrypted or crosses a proxy. A result without these conditions cannot be reproduced.
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Measure at two boundaries
Capture client-observed timings for the complete user journey and server-side or model-server timings where available. NVIDIA’s TensorRT-LLM core benchmark measures TensorRT-LLM inference but excludes third-party preprocessing and postprocessing. It is useful for isolating model-serving behavior, not as a substitute for application latency.
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Capture distributions and request dimensions
Store every request’s TTFT, individual ITL observations or TPOT, end-to-end latency, input tokens, output tokens, queue time when available, status, model, replica, and concurrency. Calculate median and tail percentiles such as P90, P95, and P99. Slice by model, context length, output length, arrival rate, concurrency, cache-hit state, and protocol. A single average can remain unchanged while a small but important tail becomes unusable.
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Change one relevant variable
After identifying a likely bottleneck, change one setting—such as maximum batch size, quantization, routing policy, or prompt construction—then rerun the same workload. Keep the dataset, arrival pattern, warm-up procedure, and measurement boundary fixed. Compare latency, throughput, quality, memory, errors, and cost together.
Instrument the request path, not only the model
A production request typically passes through a client, gateway, authentication and policy checks, prompt construction, a queue, a scheduler, model prefill, autoregressive decode, network streaming, and postprocessing. Put timestamps around each stage.
- Client submission: timestamp immediately before the request is handed to the transport.
- Gateway receipt and dispatch: record admission, routing, retries, and time waiting for a worker.
- Prompt preparation: measure template rendering, retrieval, tool-result assembly, tokenization, and safety or policy checks.
- Queue and batch admission: record time waiting for a scheduler and the batch in which the request runs.
- Prefill start and finish: measure prompt processing and KV-cache construction.
- First token: timestamp the first token delivered to the client, and, when possible, the first token produced inside the server.
- Every subsequent token: record emission and client receipt times to calculate ITL and expose pauses.
- Final token and postprocessing: record model completion, detokenization, filtering, serialization, and the final byte received.
Comparing server first-token time with client TTFT separates model and queue work from network and application overhead. If server TTFT is stable but client TTFT grows, investigate gateways, connection pools, network paths, serialization, or client-side buffering.
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LLM inference has two materially different stages. During prefill, the server processes the prompt in parallel and builds the key-value (KV) cache. During decode, it generates output tokens sequentially, reusing that cache and the tokens already produced.
Prompt-heavy workloads
Long-context summarization, retrieval-augmented prompts, and classification can spend most of their time in prefill. Their TTFT rises with input length even when output responses are short. Measure TTFT against input-token buckets and inspect prompt-processing time separately.
Generation-heavy workloads
Long-form answers, code generation, and agent traces can spend most of their time in decode. ITL or TPOT, memory bandwidth, KV-cache capacity, and scheduling interactions become more important than raw prompt-processing speed.
Mixed traffic
A service carrying both shapes can show contradictory averages: prompt-heavy requests inflate TTFT while long generations consume decode capacity and increase queueing for everyone else. Report groups separately or use a weighted distribution that reflects the actual production mix.
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Why an LLM is slow in production
Queueing and admission
At high utilization, a request can spend more time waiting for a batch slot than executing. Inspect queue time, queue depth, arrival rate, active sequences, and replica-level imbalance. A high accelerator duty cycle indicates that hardware is busy; it does not by itself prove that the accelerator is the user-facing bottleneck.
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Prompt construction and preprocessing
Retrieval, document parsing, tokenization, template expansion, tool calls, policy checks, and large serialized payloads can delay model submission. These costs are absent from many core benchmarks. Measure them independently and avoid rebuilding identical prompts on every retry or turn.
Network and protocol behavior
Extra proxies, cross-region routing, TLS setup, connection-pool exhaustion, buffering, and a client that waits for a full HTTP chunk can all inflate apparent TTFT or ITL. Compare timestamps at the gateway, server, and client, and verify that streaming is enabled end to end.
Batching policy
Larger batches often improve hardware utilization and throughput but make individual requests wait longer for admission or share decode capacity. Continuous batching can admit new work while other sequences decode, yet its scheduler policy still needs to be tested at the target load.
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Prefix-cache hits can reduce repeated prompt work, while cache misses or evictions increase prefill. Replica routing that ignores cached prefixes can turn a potentially warm request into a cold one. Record cache-hit state and KV-cache utilization in latency traces.
Memory pressure and capacity
Long contexts consume KV-cache memory. Evictions, paging, swaps, or a reduced maximum sequence length can create pauses or rejections. Track accelerator memory, KV-cache occupancy, active sequences, and out-of-memory events alongside latency.
Reduce latency in a controlled optimization sequence
1. Remove avoidable path work
- Reuse persistent connections and keep traffic in the intended region.
- Move prompt assembly, tokenization, and retrieval off the critical path where the product permits it.
- Eliminate duplicate serialization, logging of full prompts, and unnecessary gateway hops.
- Stream from the server through the gateway to the client without buffering the entire response.
- Set explicit timeouts and retry policies; an aggressive retry can multiply load and worsen the original tail.
Rerun the same workload after each change. A shorter model timer is irrelevant if the client still waits in a gateway.
2. Right-size batching
Test maximum batch size, maximum number of active sequences, scheduler delay, and continuous-batching settings as a group. Increase batch size until the latency objective is reached or throughput stops improving, then test around that boundary. The best setting is workload-specific: a batch that is efficient for short prompts can damage TTFT for long prompts, and a setting that is excellent at low concurrency can collapse under bursts.
Record both throughput and tail latency at each setting. A useful result might be more output tokens per second at the same P95 TTFT, not simply the largest possible batch.
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3. Match infrastructure to workload shape
For prompt-heavy traffic, compare prefill throughput and TTFT at the actual context-length distribution. For output-heavy traffic, compare decode pacing, memory behavior, and ITL under long generations. Validate price-performance with the same workload, model, quantization, batch policy, and quality checks rather than transferring a platform vendor’s result to another deployment.
4. Evaluate serving optimizations
Common techniques include PagedAttention, quantization, continuous batching, speculative decoding, and separating prefill from decode on different workers. Each changes trade-offs:
- PagedAttention and related KV-cache management: can use memory more efficiently and support more concurrent sequences, but measure allocator behavior and tail latency.
- Quantization: can reduce memory use and increase throughput; test output quality, numerical stability, and any change in TTFT or ITL.
- Continuous batching: can keep hardware busy while sequences finish at different times; verify that admission delays and fairness remain within the objective.
- Speculative decoding: may accelerate accepted token runs with a draft model, but gains depend on acceptance rate and add draft-model work and operational complexity.
- Prefill/decode disaggregation: can isolate prompt processing from generation, but adds transfer and scheduling paths; it is most useful when the two stages have conflicting resource demands.
Google Cloud identifies batching, PagedAttention, and quantization as serving optimizations and also discusses continuous batching, speculative decoding, intelligent routing, and prefill/decode disaggregation. Treat each as an experiment, not a guaranteed improvement.
5. Use routing and cache locality deliberately
Load-aware routing can consider queue depth and KV-cache utilization rather than sending every request by round robin. Content-aware routing can send a request with a matching prefix to a worker that already holds that prefix in cache. This can reduce prefill work, but it may also create hot spots; monitor queue balance and cache evictions.
A Google Cloud production case study reports 35% faster TTFT for Qwen3-Coder after an intelligent-routing migration for context-heavy coding workloads. The same case study reports a 52% P95 tail-latency improvement for DeepSeek V3.1 in bursty chat and an increase in prefix-cache hit rate from 35% to 70%. These are vendor-reported results for the named workloads and architecture, not a forecast for another model or traffic pattern.
6. Recheck quality, capacity, and economics
Latency is not a win if quantization changes required behavior, speculative decoding increases errors, routing reduces fault tolerance, or batching causes unacceptable cancellations. Maintain a representative golden dataset and automated evaluation so every serving change is checked for factuality, formatting, tool-use success, safety requirements, and task-specific quality. Also record cost per useful output, memory headroom, replica availability, and error rate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare servers, hardware, and configurations fairly
Use one controlled request distribution and align metric definitions before comparing systems. Separate materially different input and output lengths instead of allowing a favorable mix to determine the result.
| Comparison axis | What to report |
|---|---|
| Initial responsiveness | TTFT median and tail percentiles at the target concurrency and arrival rate. |
| Streaming smoothness | ITL or TPOT percentiles, plus the distribution of unusually long pauses. |
| Completion speed | End-to-end latency percentiles with the output-length distribution shown. |
| Capacity | Output tokens per second at the stated latency objective, requests per second, active concurrency, and total-token throughput where input sizes differ. |
| Workload conditions | Model and version, input/output lengths, arrival pattern, warm-up state, cache state, batch policy, hardware, quantization, and replica count. |
| Measurement boundary | Client-observed, gateway, server, or model-core timing; do not mix them in one ranking. |
| Resource behavior | Accelerator duty cycle, memory, KV-cache occupancy, queue depth, and capacity headroom. |
| Quality and economics | Golden-set quality, error and timeout rates, and cost per useful output under the same traffic. |
When input and output lengths vary substantially, publish separate buckets—for example, short-context/short-output, long-context/short-output, and long-context/long-output. Otherwise a high-throughput result may simply reflect easier requests.
Production monitoring that catches regressions
Build dashboards around distributions and slices, not a single latency number. At minimum, monitor TTFT, ITL or TPOT, end-to-end latency, request rate, output tokens per second, input and output token counts, queue depth, active sequences, cache-hit rate, accelerator duty cycle, and memory.
Google Cloud Monitoring documents TTFT and response-time distributions with model and context-window labels. Those particular monitoring metrics are sampled every 60 seconds and can appear up to 120 seconds later, so use them as operational signals rather than assuming they are instantaneous traces. Pair them with request-level tracing when diagnosing a short-lived incident.
Useful dashboard slices
- Model and model version
- Replica, zone, or region
- Input-token and output-token buckets
- Concurrency and arrival-rate bands
- Streaming versus non-streaming protocol
- Cache hit versus miss
- Batch size or scheduler policy
- Success, timeout, cancellation, and retry status
Alert on a sustained percentile breach and on a change in the distribution’s shape. A rising P99 with a stable median usually points to queueing, hot replicas, cache imbalance, or intermittent network work rather than a uniform model slowdown.
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Troubleshooting playbook
| Observed symptom | Likely evidence to inspect | Next action |
|---|---|---|
| TTFT rises while ITL is unchanged | Queue time, prompt length, prefill duration, gateway timing, cache misses. | Separate admission, prefill, and network delays; test shorter or cached prefixes and scheduler settings. |
| TTFT is stable but streamed text pauses | ITL distribution, decode utilization, KV-cache memory, batch composition. | Investigate decode contention, memory pressure, and batch fairness; test output-heavy traffic separately. |
| Model-server timing is good but client timing is poor | Proxy buffering, connection setup, serialization, TLS, region and network hops. | Trace each boundary and verify that partial tokens are forwarded immediately. |
| Only P95/P99 worsens during bursts | Queue depth, admission delay, replica imbalance, retries, cache hot spots. | Reproduce the arrival pattern; test load-aware routing, admission limits, and additional capacity. |
| Throughput improves but completion latency violates the objective | Batch wait, per-request decode share, tail percentiles. | Reduce or partition batches, adjust scheduler delay, and optimize for tokens per second at the allowed tail. |
| Latency changes after a prompt-template update | Input-token counts, prefix-cache hit rate, prefill time. | Compare tokenized prompts and cache keys; remove redundant instructions or stabilize shared prefixes where safe. |
| Quantization lowers latency but quality falls | Golden-set task scores, refusal and tool-use behavior, numerical errors. | Reject the configuration or use selective precision; do not trade required quality for an unqualified speed gain. |
A practical experiment record
For every benchmark or production change, retain a compact record containing:
- Objective and percentile target
- Model, checkpoint, tokenizer, and serving software versions
- Hardware type, replica count, region, and resource limits
- Quantization and attention or KV-cache settings
- Prompt and output length distributions
- Arrival process, concurrency, warm-up, and test duration
- Streaming protocol and timing boundaries
- Batch and scheduler configuration
- Cache state and routing policy
- TTFT, ITL/TPOT, end-to-end latency, throughput, errors, memory, and cost
- Quality results from the same representative dataset
This record prevents a common failure: declaring an optimization successful because one average from one tool improved while the workload, boundary, or quality requirement changed.
Use the measurement loop to balance latency and throughput
Latency and throughput are coupled. Raising concurrency or batch size can increase output tokens per second until queues and memory pressure make TTFT and tail latency worse. Reducing batch size can improve responsiveness while leaving expensive capacity unused. The correct operating point is the one that meets the product’s percentile objective at the expected traffic while preserving quality, memory headroom, availability, and cost limits.
For a new deployment, establish a baseline with representative traffic, then optimize in this order: eliminate path overhead, separate prefill from decode in the traces, tune batching, match infrastructure to the workload shape, test cache-aware routing and serving techniques, and finally validate quality and economics. Reuse the same workload after every change so an apparent gain reflects the configuration—not a different test.
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