The Tool Desk
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Start with a baseline that records time to first token (TTFT), inter-token latency, end-to-end percentiles, throughput, queue depth, accelerator utilization, memory, output quality, and cost per useful response. Then match the intervention to the measured bottleneck: scheduling and batching for underused accelerators with concurrent traffic, quantization for weight-memory and bandwidth pressure, prefix caching for repeated prompts, speculative decoding for decode-heavy traffic, and prefill/decode disaggregation when the two phases interfere.
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1. Establish a baseline before changing the serving stack
Benchmark the exact model, engine, hardware allocation, and request distribution you intend to operate. A benchmark made from one short prompt and one fixed output length can hide the queueing and memory behavior that dominates production.
Build a representative request mix
- Use the real distribution of prompt lengths, including long system prompts and retrieved context.
- Use realistic output-length limits and stopping behavior rather than an artificially fixed token count.
- Replay the expected arrival pattern: steady traffic, bursts, and quiet periods.
- Test the concurrency that your service objective requires, not only the maximum number of requests the server can accept.
- Keep model version, tokenizer, sampling settings, precision, serving engine, and accelerator count constant while comparing alternatives.
Run enough repetitions to see warm-up effects and tail behavior. Record results separately for short and long prompts, short and long generations, and low and high concurrency when those groups have different user experiences.
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Measure the metrics that explain user experience and cost
| Metric | What it tells you | How to use it |
|---|---|---|
| Time to first token (TTFT) | Waiting for prompt processing, queueing, and the first decode step | Use it to detect prefill or admission delays. |
| Inter-token latency | Time between generated tokens after the first token | Shows decode responsiveness and streaming smoothness. |
| End-to-end latency | Total request time from arrival to completion | Report median and tail percentiles such as p95 and p99. |
| Throughput | Requests or useful output tokens completed per unit of time | Measure at the target concurrency, not only at saturation. |
| Queue depth and queueing time | Work waiting for admission or an available worker | A rising queue is a capacity warning and usually increases user-facing latency. |
| Accelerator utilization and memory | Whether compute, memory capacity, or memory bandwidth is constrained | Correlate utilization with each phase instead of relying on an average. |
| Quality or output equivalence | Whether an optimization changes answer correctness or task performance | Compare against the unoptimized model with a fixed evaluation set. |
| Cost per useful output | Infrastructure cost for an accepted, quality response | Include retries, rejected outputs, idle capacity, and cache overhead. |
AWS Prescriptive Guidance states, “No single metric is sufficient on its own.” A lower average latency is not an improvement if p99 latency, quality, or cost per useful response becomes worse.
Define the service objective
Write down the objective before tuning: for example, a TTFT target, an inter-token target, a p95 completion target, a minimum throughput at a specified concurrency, and a maximum cost per accepted response. A single peak-throughput number is not a service objective. If the queue grows during the traffic pattern you care about, the deployment is undersized or its scheduling policy is mismatched even if a synthetic benchmark reports high throughput.
2. Separate prefill from decode
LLM serving has two materially different phases. During prefill, the system processes the entire input prompt and builds the key-value (KV) state. This phase is generally compute-bound and strongly affected by prompt length. During decode, the model generates one token at a time, reading model weights and KV state repeatedly; it is commonly limited by memory bandwidth.
Symptoms of a prefill problem
- TTFT rises sharply with longer prompts while inter-token latency remains acceptable.
- Accelerator compute utilization is high during prompt processing.
- Large retrieved contexts or system prompts dominate total request time.
Prioritize prompt reduction, prefix reuse, prompt-aware scheduling, or additional prefill capacity when these measurements appear. Do not expect a decode-only optimization to fix a prompt-processing bottleneck.
Symptoms of a decode problem
- TTFT is reasonable but tokens arrive slowly.
- Inter-token latency grows with output length or concurrent generations.
- Memory bandwidth is heavily used while arithmetic units are not fully occupied.
Quantization, speculative decoding, larger effective decode batches, and adequate KV-cache capacity are candidates for this pattern. Validate each one on the same output-quality test set.
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Symptoms of a capacity or scheduling problem
- Queue depth increases during bursts or remains elevated at normal traffic.
- Both TTFT and end-to-end tail latency rise with concurrency.
- Accelerator utilization is low while requests wait, indicating admission, routing, or an insufficiently populated batch.
Address worker count, routing, batching, or capacity before micro-optimizing kernels. A faster kernel cannot compensate for a queue that is growing faster than the service can drain it.
3. Choose an optimization from the measured bottleneck
| Observed condition | First interventions to test | Important trade-offs |
|---|---|---|
| Many concurrent requests; accelerators have idle gaps | Continuous batching, in-flight scheduling, and queue-aware admission | Aggregate throughput may rise while an individual request waits longer; low traffic may see little benefit. |
| Weights consume most memory or decode is bandwidth-bound | Quantization and supported optimized execution paths | Quality can change; dequantization overhead and kernel support determine the result. |
| Requests repeat a system prompt or long prefix | Prefix/KV caching and cache-aware routing | KV memory, eviction, replication, and transfer overhead can outweigh saved computation. |
| Output generation dominates latency | Speculative decoding and decode-oriented batching | Benefits depend on draft acceptance; poor proposals add work. |
| Prompt processing disrupts generation | Prefill/decode disaggregation | Requires KV transfer, networking, separate pools, and more operational components. |
| Model does not fit or memory headroom is unsafe | Sharding, a different accelerator allocation, quantization, or a smaller model that meets quality targets | Inter-device communication, quality, portability, and total cost can change. |
Change one major variable per experiment. Keep the request mix and target concurrency fixed, warm the system consistently, and compare both medians and tails. Retain an optimization only when it improves the service objective without unacceptable quality or operational regressions.
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Continuous, also called in-flight, batching admits new requests into an active generation batch as other requests finish. Unlike a static batch that waits for every member to complete, it keeps the accelerator supplied with useful work as requests have different output lengths.
When it helps
It is most useful when several requests are decoding concurrently and the accelerator has idle capacity between individual generations. Measure throughput at the concurrency your service actually receives, then inspect queueing delay and p95/p99 latency.
When it does not
At low traffic there may not be enough simultaneous requests to form a productive batch. At high contention, a policy that maximizes aggregate tokens can increase the time a particular request waits for its turn. NVIDIA’s inference-optimization guidance and AWS Builder Center both emphasize that batching gains are traffic-dependent; neither throughput improvement nor a vendor-reported result guarantees lower latency for every request.
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How to evaluate a batching change
- Run the baseline and batched engine against the same arrival trace.
- Compare TTFT, inter-token latency, p95 and p99 completion time, and throughput at each tested concurrency.
- Check whether long generations monopolize slots and whether short requests suffer head-of-line delay.
- Set queue and timeout limits so overload fails predictably instead of allowing unbounded waiting.
- Choose the policy that meets the latency objective at the required throughput, not the policy with the highest isolated tokens-per-second figure.
5. Apply quantization and compilation carefully
Quantization stores weights at lower precision, reducing memory use and the amount of data moved during inference. The freed memory can support a larger batch or more KV cache, and lower movement can improve decode performance. The actual result depends on the model, hardware, supported numeric formats, kernels, and any dequantization work performed during execution.
A safe quantization workflow
- Record full-precision quality on a representative evaluation set for your task.
- Select a precision and quantization method supported by the target accelerator and serving engine.
- Measure memory footprint, TTFT, inter-token latency, throughput, and tail latency under the production request mix.
- Re-run quality checks, including factuality, structured-output validity, tool-call correctness, and safety tests relevant to your application.
- Compare cost per useful output, not merely the hourly price or raw token rate.
Compilation and optimized execution modes can reduce runtime overhead when the selected engine and hardware support them. Compilation may have a warm-up cost, shape restrictions, or a separate path for dynamic sequence lengths. Benchmark cold-start and warmed behavior if you scale workers frequently.
6. Use speculative decoding for decode-heavy traffic
Speculative decoding uses a smaller draft model, or another inexpensive proposal mechanism, to suggest several tokens. The target model verifies those candidates in a batch and accepts the matching prefix. When proposals are cheap and accepted often, the target performs fewer sequential decode steps.
Where it is promising
Test it when output generation dominates latency, inter-token latency is the limiting metric, and the draft model predicts the target model reliably. The technique is less useful when prompt processing dominates or when draft acceptance is poor.
What the benchmark does—and does not—prove
An AWS Machine Learning Blog example published April 15, 2026 reports up to 3x acceleration for decode-heavy workloads in a specific Qwen3, vLLM, and AWS Trainium configuration. That figure applies to the target and draft models, software, hardware, and workload described in that example; it is not a universal speedup promise. Measure acceptance rate, target-model work, memory use, quality, and tail latency on your own deployment.
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Controls to include
- Track the average number of draft tokens proposed and accepted.
- Measure extra draft-model memory and compute.
- Test short and long outputs separately.
- Keep a fallback path for requests where proposals are consistently rejected.
- Verify exact or task-level output equivalence, depending on whether sampling makes byte-for-byte comparison inappropriate.
7. Add prefix and KV caching when prompts actually repeat
Prefix caching stores KV blocks for a shared beginning of a request, such as a system prompt, policy text, or stable retrieved preamble. A later request with the same prefix can skip recomputing that portion during prefill. Cache-aware routing sends a request to a replica that already holds the relevant blocks, increasing the chance of a hit.
Measure reuse before reserving memory
Log prefix identifiers, hit rates, evictions, cache residency, and the prompt tokens saved per hit. A low hit rate, rapidly changing prompts, or a cache that evicts useful blocks under pressure can make the memory cost larger than the compute saved.
Account for the trade-offs
- KV blocks consume accelerator memory that could otherwise hold weights or active sequences.
- Replicated caches can duplicate memory across workers.
- Routing to a cache-owning replica may conflict with load balancing.
- Moving cached state between workers adds transfer overhead.
- Cache keys must respect tenant isolation and prompt confidentiality.
Enable caching for measured, high-reuse prefixes and define eviction and invalidation rules. Treat cache capacity as part of the serving budget, not as free performance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Consider prefill/decode disaggregation only after phase interference is proven
Disaggregated serving places prefill and decode on separate worker pools. Prefill workers process complete prompts and produce KV state; decode workers generate tokens. Because prefill is compute-intensive and decode is commonly bandwidth-sensitive, colocating them can cause one phase to interfere with the other.
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- Scale prefill and decode independently as their traffic or resource needs change.
- Protect decode latency from bursts of long prompts.
- Choose hardware or scheduling policies specialized for each phase.
Costs and failure modes
- KV state must be transferred between pools, consuming network bandwidth and adding delay.
- Routing, placement, retries, and observability become more complex.
- Small or low-concurrency deployments may pay the overhead without enough interference to remove.
- A network bottleneck can erase the gain from separating compute phases.
Prototype this architecture only when traces show prompt bursts or phase contention affecting the service objective. Compare a colocated baseline and the disaggregated design with identical request mixes and include transfer time in TTFT and end-to-end measurements.
9. Size models, KV cache, and accelerators together
Capacity planning must fit model weights, runtime buffers, active sequences, and KV cache in available accelerator memory with headroom for traffic variation. Prompt length, output length, concurrency, and batching policy determine how much KV state is live. A configuration that fits a single short request may fail when several long generations overlap.
Sharding and communication
Sharding can distribute a model across multiple GPUs when it cannot fit on one device. It also introduces inter-device communication that can reduce throughput or increase latency, especially for small batches or communication-constrained topologies. Benchmark the complete sharded deployment rather than extrapolating from single-device specifications.
Compare infrastructure by total operating result
| Option | Evaluate | Questions to answer |
|---|---|---|
| Managed inference service | Provisioning, autoscaling, supported engines, observability, and per-request pricing | Can it meet TTFT and tail targets at the required concurrency without hidden idle capacity? |
| Managed container or cluster | Control over runtime, batching, caching, and placement | How much cluster and networking work is required to obtain that control? |
| Self-managed accelerators | Utilization, fleet operations, upgrades, failures, and capacity commitment | Does the utilization justify the operational burden and fixed capacity? |
AWS Prescriptive Guidance documents accelerator examples including H100 configurations, but an example accelerator is not a universal purchasing recommendation. Compare alternatives with the same model version, request distribution, concurrency, quality gate, and accounting period.
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Infrastructure cost includes accelerator time, host and networking charges, storage, idle replicas, cache memory, orchestration overhead, and retries. Divide the total by responses that pass your quality and validity checks. A cheaper configuration that produces more failed or repeated requests can cost more per useful result.
Cost controls that preserve the service objective
- Right-size the worker pool to the measured traffic profile and autoscaling target.
- Set maximum prompt and output lengths where the application can tolerate limits.
- Use batching and caching only when their measured hit rate or utilization offsets their memory and queueing costs.
- Separate latency-critical traffic from background jobs so batch workloads do not consume interactive capacity.
- Track idle time, queue time, and accelerator memory reservation in addition to billed runtime.
- Re-test after model, tokenizer, engine, or traffic changes; cost curves can shift without a hardware change.
11. Run controlled experiments and roll out safely
- Freeze the baseline. Store model artifacts, engine settings, hardware allocation, request trace, quality set, and metric definitions.
- Form one hypothesis. For example: “Prefix caching will lower TTFT for requests sharing the 2,000-token system prefix without exceeding the memory budget.”
- Change one major variable. Do not combine quantization, a new batch policy, and a new GPU generation in one comparison.
- Warm and repeat. Distinguish compilation or model-load time from steady-state behavior, and run enough repetitions for tail percentiles.
- Check quality and safety. Reject an optimization that violates output correctness, structured-format validity, or policy requirements even if it is faster.
- Canary the winner. Send a small share of production traffic while watching queue depth, errors, cache behavior, and p99 latency.
- Keep a rollback. Preserve the previous engine, precision, and routing configuration until the new path is stable across normal and burst traffic.
12. A practical order of operations
For most teams, the following sequence minimizes wasted effort:
- Define the latency, throughput, quality, and cost objectives.
- Capture a representative baseline and split prefill, decode, and queueing measurements.
- Fix obvious capacity or admission problems that create a rising queue.
- Test continuous batching at target concurrency if concurrent traffic leaves accelerator gaps.
- Test quantization or compilation when memory movement, weight footprint, or decode bandwidth is limiting.
- Measure prefix reuse and enable KV caching only when hits justify the memory and routing cost.
- Test speculative decoding for decode-heavy workloads with a draft model that has high acceptance.
- Evaluate prefill/decode disaggregation only when traces demonstrate phase interference and the team can operate the added networked components.
- Compare infrastructure options by cost per useful response and operational effort, then repeat the benchmark after every material workload or model change.
This order is not a promise that one technique will win. It is a way to make each decision traceable to an observed constraint and to prevent a headline benchmark from replacing measurements on your service.
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