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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsTo reduce AI latency, first measure where time is going: before the first token, between generated tokens, or across the complete workflow. Then change the part causing the delay. Shorter prompts will not fix a slow tool call, and higher token throughput does not necessarily make one request finish sooner.
Measure the latency your users actually experience
Three measurements describe different parts of a request. Record all three for representative inputs, rather than relying on a single average or a provider’s throughput figure.
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| Measure | What it captures | What a high value may point to |
|---|---|---|
| Time to first token (TTFT) | Time from submitting a query until the first output token arrives. NVIDIA’s NIM LLM benchmarking documentation says this can include queueing, prefill and network delay. | Long input processing, queueing, network delay, or slow prefill. A long prompt can increase prefill time because the model processes the input sequence before generation begins. |
| Inter-token delay | The time between successive output tokens during generation. | A slow decode or generation phase, or a serving configuration that prioritizes other objectives. |
| End-to-end latency | Time from query submission until the final response. NVIDIA’s documentation includes queueing, batching and network effects in this interval. | Any delay along the complete path: orchestration, tools, queueing, model work, or response delivery. |
Set a latency objective for the workflow before tuning. A user waiting for a brief answer may care most about TTFT; a downstream system that cannot act until a complete structured response arrives may care more about end-to-end time. Measure tail percentiles as well as typical performance: a faster average can still conceal a slow experience for a meaningful share of requests.
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Find the bottleneck before changing settings
- Define the workload and target. Choose representative requests and establish the latency objective and acceptable quality and error rates. Include the prompt and output lengths, arrival pattern and concurrency you expect in production.
- Instrument the complete request. Capture TTFT, inter-token delay and end-to-end time. If the workflow calls tools or multiple models, record the duration of each step so model time is not confused with orchestration or network time.
- Segment the results. Compare requests by prompt length, output length, concurrency and traffic pattern. This can reveal, for example, whether only long-context requests have slow TTFT or whether delays grow under load.
- Locate the dominant delay. Establish whether it occurs before the first token, during generation, in a tool or orchestration step, or in queueing and network delivery.
- Change one relevant factor at a time. Repeat the same workload and compare speed, task quality and failures. Recheck under realistic concurrency, including tail behavior, before treating an improvement as useful.
There is no universal latency target or best configuration: the right threshold depends on the application’s users and downstream requirements. A benchmark at low concurrency may not predict what happens when real traffic arrives.
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Reduce unnecessary work in the application
Application changes are often the most direct place to start. OpenAI’s Latency optimization guide groups its advice around processing tokens faster, generating fewer tokens, using fewer input tokens, making fewer requests, parallelizing work, making users wait less, and not defaulting to an LLM.
- Remove input that does not help the task. Eliminate duplicated, stale or irrelevant prompt context, but retain information needed for correctness. Shorter input can reduce prefill work and may improve TTFT.
- Ask for only the output the workflow needs. Avoid unnecessary explanations, repeated fields or excessive formatting. Do not impose a shorter response if it risks omitting information the task requires.
- Remove avoidable model calls. Combine or eliminate redundant steps only when the resulting quality and reliability remain acceptable. Extra calls can add their own model and coordination time.
- Parallelize independent work. If separate checks do not depend on one another’s results, running them concurrently can avoid waiting for each to finish in sequence. Preserve ordering where later steps rely on earlier output.
- Use ordinary code for deterministic tasks. Routing fixed rules, simple transformations or other deterministic work to code can avoid model latency where an LLM is unnecessary.
- Show useful partial output when appropriate. Streaming can make results visible sooner, but it changes when users see the response; it does not by itself establish that the final answer is computed sooner. Compare TTFT and completion time separately.
OpenAI also describes predicted outputs as an option when much of a response is already known, so the model can focus on changed content. Whether it helps depends on the task and implementation; benchmark it rather than assuming it will reduce end-to-end time.
Select a model that meets the quality bar
Smaller models usually run faster, according to OpenAI’s latency guide, but speed alone is not a sufficient reason to switch. Test candidate models on representative tasks, including difficult and failure-prone cases, and compare quality and errors alongside all three latency measures.
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Tune the inference-serving stack to the measured phase
Inference commonly has a context or prefill phase followed by decode or generation. NVIDIA’s TensorRT-LLM Disaggregated Serving documentation notes that optimizing TTFT can trade off against time per output token. Serving techniques therefore need to be tested against the particular objective, not treated as free speedups.
| Technique | When it may help | What to validate |
|---|---|---|
| Dynamic or continuous batching | When serving multiple requests and improving overall throughput is important. | Queueing and per-request TTFT and completion time at expected traffic levels. Higher throughput does not guarantee a faster individual request. |
| Quantization | When reduced numerical precision is supported by the model and runtime and could improve serving efficiency. | Output quality, hardware behavior, runtime support and latency under the target workload. |
| Speculative decoding | When the serving system supports a compatible draft-and-target approach that can accelerate generation. | Compatibility, acceptance behavior, quality and actual inter-token and completion times. |
| Prefix or KV-cache reuse | When requests share reusable context and the model-serving stack supports reuse. | Whether prompts really repeat enough to benefit, plus cache behavior, correctness and TTFT. |
| Routing | When requests can be directed to an appropriate model or serving path based on the task or context. | Quality and latency for each request class, routing overhead and behavior under the expected traffic mix. |
| Separate prefill and decode serving | When profiling shows that separating context processing from generation could better match the workload. | TTFT, time per output token, complete-request latency and the operational effects of the split. |
Google Cloud’s engineering article on inference efficiency presents serving techniques as choices along a latency-throughput frontier. In a reported Vertex AI engineering case study, it describes a 35% reduction in TTFT and doubled cache efficiency for its particular routing case. That is a vendor-reported outcome for the deployment described in that article, not a general forecast or an independently established result for other models and workloads.
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Consider hardware after profiling
Hardware is worth investigating when measurements show the model-serving compute path is the constraint. OpenAI’s Latency optimization guide says: “Most people can’t influence these factors directly, but faster hardware or running engines at a lower saturation may give you a modest TPM boost.” This is a qualified observation, not a prediction that a particular accelerator will fix a slow workflow.
Before comparing hardware or deployment options, benchmark the target model and precision with the workflow’s prompt and output lengths, concurrency and deployment topology. Compare TTFT, inter-token delay, end-to-end latency and throughput, along with quality, failure rate, hardware requirements, operational complexity, cost, geography and data-handling needs. The outcome for one setup does not establish a result for another.
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Use a repeatable decision loop
- Write down the objective. Specify which latency measure matters most and what quality and reliability the workflow must preserve.
- Establish a baseline. Run realistic requests at representative concurrency and retain the measurements by request type.
- Choose an intervention that matches the bottleneck. For example, investigate prompt length when TTFT is high for long inputs; examine model choice or serving when generation is slow; inspect orchestration and tools when model timings do not account for completion time.
- Compare like with like. Keep the workload and measurement method consistent, and change one factor at a time where practical.
- Keep only improvements that survive validation. Check quality, failures and tail latency under realistic load as well as the target average or throughput. Reassess when traffic, prompts, models or serving configurations change.
Google Cloud’s “Five techniques to reach the efficient frontier of LLM inference” and the NVIDIA and OpenAI documentation cited above were accessed October 7, 2026. Their published guidance is useful for choosing experiments; only measurement on the workflow’s own traffic can establish which change meets its latency and quality requirements.
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