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This guide covers how to split latency into measurable parts and how to set a budget. It then maps application-level changes, model choice, serving-stack techniques and streaming to the bottleneck each one addresses. Where a source reports a number, the article gives the context it came from. Those numbers are not forecasts for your model, stack, traffic or region.
Measure three things before changing anything
“Latency” means different things to different users. A person watching a response appear cares about when the first words show up. A pipeline that needs the whole answer before its next step cares about when the last token arrives. NVIDIA’s NIM benchmarking documentation separates these, and you should too.
| Metric | What it covers | Who feels it |
|---|---|---|
| Time to first token (TTFT) | Query submission to the first received token. Per NVIDIA, this includes queue time, prefill and network latency. | Interactive users, and any workflow that can act on partial output |
| Inter-token delay | The gap between successive output tokens while the model generates. NVIDIA’s serving documentation frames this as the decode stage, where time per output token can trade against TTFT. | Anyone reading a stream, plus any step that consumes tokens as they arrive |
| End-to-end latency | Query submission to the final response. NVIDIA notes it includes queueing, batching and network effects. | Downstream code, automated pipelines, and users who need the complete answer |
Two consequences follow from NVIDIA’s definitions.
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- Prompt length drives TTFT. Before generating, the model must process the whole input to build the KV cache (the stored attention state it reuses while generating). This is the prefill step, so a long prompt tends to raise TTFT even if generation is quick afterward.
- Throughput is not latency. A system can produce many tokens per second across all users and still be slow for one request. NVIDIA’s documentation on metrics and disaggregated serving both stress that you should define the latency objective that matters to your workflow and benchmark it at realistic concurrency.
Set a latency budget and a fair test
Your target, acceptable quality loss and traffic shape are decisions only you can make. No source supplies them. Without them, “faster” has no meaning. Work through these steps:
- Write the budget. Decide the maximum acceptable wait for the first token, for a complete answer, or both. Use the step that actually blocks the user or the next system. Include percentiles such as p95 or p99 where a late response is a failure, not only the average.
- Collect representative requests. Use real prompts if you can, including your longest ones and the awkward cases where quality tends to fail.
- Record all three metrics per request. Timestamp submission, first token and last token. Also record input length, output length and the number of requests in flight.
- Segment the results. Group by prompt length, output length, concurrency and time of day. A blended average will hide the segment that breaks your budget.
- Test at expected load. A configuration that looks fast with one request at a time can behave very differently once requests queue.
- Change one thing at a time. Re-run the identical workload, and track task quality and error rate next to speed. A faster answer that is wrong more often is not an improvement.
- Recheck the tail. A change that improves throughput or the mean can still worsen the slowest requests, which are often the ones a time-critical workflow cares about.
Match the symptom to the likely bottleneck
Use this table as a starting hypothesis. Each row still needs confirming with your own measurements.
| What you observe | Likely location | First things to test |
|---|---|---|
| High TTFT, especially for long prompts | Prefill (input processing) | Trim input tokens; reuse a shared prefix where the runtime supports it; consider separating prefill from decode |
| TTFT rises sharply as traffic rises | Queueing or batching delay | Review batching behavior and capacity; check for saturation |
| TTFT varies by region or client | Network | Compare against measurements taken near the serving endpoint |
| First token is quick but the answer finishes late | Decode (token generation) or output length | Cap or shorten output; try a smaller model; test speculative decoding or quantization |
| Model calls look fast, yet the workflow is slow | Orchestration: sequential calls, tool calls, retries | Remove or merge calls; run independent steps in parallel; move deterministic work to ordinary code |
Reduce the work your application sends to the model
OpenAI’s latency optimization guide organizes its advice into seven principles: process tokens faster, generate fewer tokens, use fewer input tokens, make fewer requests, parallelize, make users wait less, and don’t default to an LLM. Most of these cost nothing in infrastructure, so they are usually the best place to start.
Generate fewer tokens
Output tokens are produced one after another, so output length often dominates total completion time. Constrain responses to what the task needs. Ask for a label instead of an explanation if only the label is used, and use structured fields instead of prose. Do not force short answers where brevity breaks correctness.
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Remove prompt context the model doesn’t use, such as stale instructions, duplicated documents and unused examples. This mainly improves TTFT because there is less to prefill. Don’t cut context the answer depends on.
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Make fewer requests
Look for redundant calls: a classification followed by a second call that could have been part of the first, or a verification step that adds latency without catching errors. Combine or eliminate calls only where quality holds up in your tests.
Parallelize independent steps
If two sub-tasks don’t depend on each other’s output, run them concurrently so the workflow waits for the slower one, not the sum of both. Steps that depend on earlier results can’t be parallelized this way.
Skip the LLM when code will do
Validation, formatting, lookups, routing on fixed rules and arithmetic are deterministic. Ordinary code handles them faster and more predictably than a model.
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Use predicted outputs when most of the answer is known
OpenAI’s guide describes a predicted-outputs feature for cases where much of the output is known in advance, such as making small edits to an existing document. The model can then focus on the parts that change. Check current availability and model support in OpenAI’s documentation before designing around it.
Choose a model that is only as large as the task requires
OpenAI’s guide notes that smaller models usually run faster, and that model size is an important speed factor. The risk is quality. Pick the smallest model that clears your quality bar on representative tasks, and test it on the difficult and failure-prone cases, not only the easy ones.
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The guide also suggests ways to keep a smaller model accurate:
- longer, more detailed prompts
- few-shot examples
- fine-tuning or distillation
Note that detailed prompts and examples add input tokens. That can raise TTFT, so measure the net effect on your workload.
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If you control the runtime, the serving layer can change both the first-token and generation phases. NVIDIA describes inference as a context (prefill) stage followed by decode, and notes that optimizing TTFT can trade against time per output token. Google Cloud’s engineering article on inference techniques likewise treats these methods as points on a latency-and-throughput frontier, not free speedups. The techniques below are experiments to run, not settings to switch on blindly. Confirm that each is supported by your model and runtime, and re-check quality after every change.
Batching
Dynamic or continuous batching groups requests to use hardware efficiently, which generally raises throughput. The cost is that requests may wait for others, so queue time can rise. For a time-critical workflow, judge batching by tail latency at your real arrival rate, not by tokens per second.
Quantization
Quantization stores model weights, and sometimes other values, at lower precision to reduce memory use and potentially speed up inference. The effect depends on your hardware and runtime, and it can change output quality. Evaluate both on your own tasks.
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Speculative decoding
A smaller draft model proposes tokens that the larger target model then verifies, which can speed up generation. The benefit depends on the draft and target models being compatible and on how often the target accepts the draft’s proposals. Poor acceptance can erase the gain, so test it on your real prompts.
Prefix and KV-cache reuse
Caching helps only when context is actually reusable, for example a long shared system prompt or a common document prefix. If every request has a unique prefix, expect little benefit. Where your runtime supports reuse, structure prompts so the stable content comes first and the variable content last.
Separating prefill from decode
Prefill and decode stress hardware differently, which is why NVIDIA’s TensorRT-LLM documentation covers disaggregated serving, where each stage runs on separate resources. It targets the tension between TTFT and per-token speed, and it adds deployment complexity. Treat it as an option for when profiling shows the two phases interfering with each other, not as a default.
Hardware, after profiling
It is tempting to buy your way out. OpenAI’s latency guide is cautious about this:
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The quote is from OpenAI’s Latency optimization guide and is not attributed to a named person. It is a qualified statement, not a promise that any particular GPU fixes latency. Before choosing hardware, you need the target model, precision, memory requirements, typical prompt and output lengths, concurrency, deployment topology and a representative benchmark. For a local setup, those checks come first. No specific GPU or expected improvement is established by the sources here.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use streaming, and know what it does and doesn’t do
Streaming delivers partial output as it is generated. It can make a workflow feel much more responsive, and it lets downstream steps begin on early output if that is safe. But it changes when output becomes visible, not when the model finishes. It does not by itself show that the final answer is computed sooner.
So track TTFT and full-response time separately. Streaming helps a human reader. It helps an automated pipeline only if the next step can act on partial results. If the next step needs the complete answer, focus on end-to-end time instead.
Read benchmark numbers with their context
Published figures are useful as evidence that an approach can work, not as expectations for your system. Take the one named number this article relies on. Google Cloud’s 2026 engineering article reports a 35% TTFT reduction and doubled cache efficiency in a Vertex AI case involving routing. That is a vendor-reported result for one deployment. It is not an independent benchmark, and it doesn’t tell you what a different model, serving stack, traffic pattern or region would see.
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The same caution applies to any throughput figure. NVIDIA’s documentation notes that high token throughput does not ensure a fast first response or fast completion for a single request. Always ask three questions of a quoted result: what workload was it measured on, at what concurrency, and was quality held constant?
Compare candidate setups on the same axes
When you’re weighing models, runtimes or deployment options, put them through the same test and compare:
- TTFT, inter-token delay and end-to-end latency, including tail percentiles under realistic concurrency
- task quality and failure rate on representative inputs
- prompt and output limits, context handling and cache reuse for your actual workflow
- throughput and queue behavior at your expected arrival rates
- hardware needs, operational complexity, cost, geography and data-handling requirements
The sources cited here establish the metrics and the need for trade-off analysis. They do not offer universal head-to-head measurements of particular vendors or models, so you will need to produce that comparison with your own workload. If you would rather not operate serving infrastructure, managed inference platforms are one category to evaluate against these same criteria.
A sensible order of attack
- Fix the measurement: three metrics, segmented, at realistic load.
- Remove avoidable work: unneeded input, long outputs, redundant calls, sequential steps that could be parallel, and tasks that never needed a model.
- Test a smaller model against your hardest cases.
- Add streaming if a person or a partial-consuming step benefits.
- Only then tune the serving stack, one technique at a time, watching tail latency and quality.
- Consider hardware last, with profiling evidence in hand.
This order puts the cheapest and most reversible changes first. Serving changes and hardware add cost and complexity, and they should be justified by a measured bottleneck.
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