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Optimize latency as an end-to-end reliability objective, not a race for the fastest GPU. Define what users must receive, measure the full request path under realistic load, then remove the bottleneck the measurements reveal—whether it is unnecessary model work, a queue, network overhead, or insufficient capacity. Recheck task quality and failure behavior after every change.
Define what “real time” means for your application
Latency can mean the time until a user sees the first useful output, the time until a complete response or action is ready, or the slow end of the response distribution. These are different objectives. For interactive generation, distinguish time to first token (TTFT) from end-to-end request latency, and track tail percentiles such as P95 and P99. A system can return an initial token quickly while still taking too long to finish, or meet its average while a meaningful share of users wait much longer.
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Set an application-specific service-level objective (SLO) for the user-visible operation. Specify acceptable first-result and completion times, the relevant percentiles, throughput, and error expectations. If the service must continue during an accelerator, zone, or dependency failure, include that requirement in the design. There is no universal latency target for “mission-critical”: the right thresholds depend on the task, user experience, contractual commitments, and failure tolerance. AWS’s inference guidance identifies TTFT, end-to-end latency, P95, and P99 as useful measures, but does not set one target for every application.
If “AI” means a non-generative system—such as speech recognition, computer vision, conventional prediction, robotics, or edge control—use the same end-to-end SLO principle, but select metrics and optimization methods suited to that system. Much of the serving guidance below concerns generative-model inference, especially LLMs on GPUs.
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Find where the request actually spends time
Instrument the complete service path rather than timing only the model call. Depending on the architecture, account for request arrival, queue wait, preprocessing, model prefill and generation, external tools or APIs, network transfer, and post-processing. For streaming responses, record when useful content first reaches the client as well as when the operation completes. Keep latency visible alongside throughput, queue depth, errors, and task quality; a faster response is not a win if it becomes less reliable or less correct.
Build a repeatable load test from the traffic the service is expected to handle: realistic prompt and output lengths, request mix, concurrency, and burst patterns. Include more than isolated single requests. Databricks’ production guidance recommends load testing to identify bottlenecks and validate latency and throughput requirements, while AWS cautions that workload shape matters when comparing inference configurations.
Use the measurements to locate the limiting stage. If queue wait dominates, model-level tuning may not help. If model generation dominates, serving or output changes may matter more. If an external dependency or post-processing step controls completion time, a faster accelerator alone cannot resolve it.
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Remove unnecessary work before adding hardware
Start with the application path. Each serial operation that can be removed, merged, or run concurrently can reduce user-visible delay without requiring a faster model. OpenAI’s latency optimization guide recommends making fewer requests, parallelizing independent work, shortening outputs, and avoiding an LLM for tasks better handled by ordinary code or precomputation. Its succinct advice is: “Don’t default to an LLM.”
- Combine avoidable sequential model calls when one well-designed request can do the job.
- Run independent calls in parallel instead of waiting for them one by one.
- Use code, fixed responses, cached results, or precomputed values for deterministic or constrained repeated tasks where appropriate.
- For long outputs, stream tokens so users can begin consuming useful content sooner. Consider chunking when moderation, translation, or other post-processing would otherwise hold the whole response.
Streaming can improve perceived time-to-value without reducing the total computation or completion time by the same amount. It should be treated as a user-experience measure, not proof that the full operation has become faster.
Reduce model work while protecting quality
Once avoidable application work is removed, test whether a smaller suitable model, a shorter output limit, or more relevant context can meet the task’s quality floor. OpenAI notes that generation is often the highest-latency step and gives reducing output-token count as a directional latency heuristic. It also cautions that trimming prompt size alone often produces a smaller improvement unless contexts are very large. Actual results vary with the model and workload, so validate the change rather than assuming a fixed speedup.
For each candidate, evaluate task quality and safety on representative cases alongside latency, throughput, and errors. A faster model, shorter answer, or lower-precision configuration is not acceptable if it materially harms the application’s outcomes.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Tune queues, batching, concurrency, and capacity together
Batching can improve throughput and per-request efficiency, but it is a trade-off: requests may wait for a batch to form, which can undermine an interactive latency objective. Compare batch sizes and concurrency under representative request lengths and load, looking at latency distributions as well as throughput. A large batch is not automatically the best choice for a low-latency service.
Plan baseline capacity for expected steady traffic, typical bursts, and the loss of individual accelerators where required. Autoscaling is useful for demand variation, but provisioning takes time and cold starts can create delays during sudden surges. As AWS puts it: “Auto scaling should be viewed as a mechanism for handling changes in demand rather than replacing baseline capacity planning.” For interactive workloads, scaling signals based on measures such as TTFT or tail latency may be more informative than generic resource utilization alone.
Reduce client, network, and dependency overhead
When the model is not the only bottleneck, optimize the surrounding service path. Databricks’ production optimization guidance covers connection pooling, payload size, external API latency, errors, and exponential backoff. Reusing connections can avoid repeated setup work; keeping payloads lean can reduce transfer and processing costs; and profiling external calls can reveal time spent outside inference.
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Choose managed or self-hosted inference by measured fit
Managed cloud inference and self-hosted open-model serving are both viable directions, but neither is a universal winner. Compare candidate deployments at expected peak concurrency and with the same workload. Consider measured TTFT and tail and completion latency, task quality, availability and recovery needs, data-location or deployment constraints, control over model and serving behavior, operational burden, and cost at expected utilization. Application-level reductions such as fewer calls or streaming can benefit either approach.
AWS provides one sizing illustration for a workload with approximately 3,000 tokens per second at peak. These are AWS’s estimated instance counts and throughput figures for that example, not general guarantees or directly comparable results across different workloads, quantization settings, or serving frameworks:
| AWS instance example | Throughput stated in AWS’s illustration | Estimated instances for the approximately 3,000-token-per-second peak example |
|---|---|---|
| G6e (L40S) | 800 tokens per second | Approximately 4 instances |
| P5 (H100) | 1,500 tokens per second | Approximately 2 instances |
| P5en (H200) | 1,650 tokens per second | Approximately 2 instances |
These figures come from AWS right-sizing guidance. AWS explicitly cautions that results from different workloads, quantization, or serving frameworks are not directly comparable. Use the example as a planning input to investigate, not as a capacity promise or evidence that a particular accelerator is best for your service.
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Change one meaningful factor at a time where practical, then rerun the same workload and quality checks. Record the model, prompt and output shape, quantization, serving framework, hardware, concurrency, latency distribution, throughput, quality results, and errors for each configuration. This makes comparisons interpretable and exposes trade-offs that a single latency number would hide.
Before rollout, check that the faster configuration still meets the application’s quality and reliability requirements and retains necessary capacity headroom. Keep a known-good configuration and a way to roll back if production latency, errors, or task outcomes regress. The cited vendor examples can inform what to test; they do not establish a universal hardware winner, batch size, model, or latency target.
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