Decomposing LLM Inference Latency for Better Serving Performance

TL;DR

Latency can be split into queue vs compute, and model time into prefill vs decode.
This split helps explain why TTFT, inter-token latency, and throughput can move differently.
Start by measuring each component, then tune batching, KV cache, and quantization with evaluation.

Example: A user sees a slow first response. The GPU looks active. The delay can come from waiting or computation.

TL;DR

What changed / what’s the core issue? LLM inference latency is not one number. It can be split into prefill and decode. Serving latency can also be split into queue and compute.
Why does it matter? Even on the same GPU, settings can shift TTFT, inter-token latency, and throughput differently. Those shifts can oppose each other.
What should the reader do? Measure queue vs compute and prefill vs decode first. Then tune KV cache limits and batching together. Apply quantization only after quality evaluation.

Teams running LLM services often see users waiting for the first token. The GPU can look busy. Perceived speed may not improve.

A useful first step is to locate where latency forms. That usually means stage-by-stage measurement. Only then do levers like batching, caching, and quantization become clearer.

This article summarizes a framework for GPU inference optimization. Inference can split into prefill and decode. Serving latency can split into queue time and compute time.

Current state

In LLM serving, latency is not only one inference time. It can show up differently across stages. The request flow is often split into prefill and decode.

Prefill processes the full prompt. It is the segment that produces the first output token. Decode is the repeated segment that generates one token at a time afterward.

This distinction matters because optimization points differ. Prefill often affects TTFT. Decode repeats token generation, so cumulative cost can grow.

In decode, batching can increase throughput. But mixed prefill and decode workloads can change TTFT. They can also change inter-token latency, depending on scheduling.

End-to-end server latency includes more than model computation. It can include waiting before execution. NVIDIA Triton describes latency components like queue and compute.

Queue time is time waiting for scheduling. Compute time is time spent executing inference, including data copies. This frame helps before decisions like adding GPUs.

Analysis

Batching, caching, and scheduling interact. That interaction can mislead single-technique tuning. Measurement can reduce guesswork.

The KV cache stores keys and values from previous tokens during decode. That can avoid recomputation for the next token. It can reduce compute and raise throughput.

KV cache also uses memory. Memory usage grows with batch size and context length. If memory becomes tight, concurrency can drop, and latency can rise.

So “cache makes it faster” can be incomplete. A more careful claim depends on the memory ceiling. Cache pressure can reduce concurrency and hurt speed.

Batching strategy should be evaluated similarly. NVIDIA describes in-flight batching. It mixes prefill and decode work on the GPU. This can reduce GPU idle time.

But KV cache growth can constrain how many requests fit. Then “increase batch size” can hit memory limits. Some systems report paged KV cache approaches, like PagedAttention.

Paged KV cache is reported to reduce waste and fragmentation. It aims to support more concurrent requests. It is also reported to limit per-token latency growth with longer sequences.

The effect size can vary by workload and implementation. That variation suggests measurement before broad conclusions.

Compression methods include quantization, pruning, and distillation. They can reduce GPU time. Each comes with different risks and costs.

NVIDIA TensorRT notes quantization can affect quality. It cites rounding and clamping error as causes. That implies a speed and quality trade-off.

A Scientific Reports paper discusses pruning limits. It notes unstructured sparsity may not map to GPU speedups. Many GPUs are tuned for dense computation.

ArXiv studies describe distillation trade-offs. They report a tension between task loss and distillation loss. They also report possible mismatches between accuracy and inference fidelity.

So compression choices often benefit from control and measurement. They can use service-specific quality criteria. They can also use latency and cost targets.

Practical application

A common mistake is optimizing by technique names. A steadier approach starts with a measurement framework. Triton suggests splitting serving latency into queue vs compute.

Split model time into prefill vs decode as well. Then measure each segment. Choices like batching changes or quantization become easier to justify.

A practical order can look like this. If decode throughput is the issue, check KV cache memory ceilings. If TTFT spikes, inspect prefill and decode mixing.

Consider quantization, pruning, or distillation after confirming compute is the bottleneck. Quality degradation can be possible with quantization. That risk suggests evaluation before production use.

Checklist for Today:

Measure server latency as queue time and compute time.
Measure model time as prefill and decode, then compare TTFT and inter-token latency.
Define quality criteria for compression, then test configurations before production changes.

FAQ

Q1. Why do we need to separate prefill and decode?
A. An LLM request has two stages. Prefill covers prompt processing and first token generation. Decode generates subsequent tokens one by one. The levers can behave differently in each stage.

Q2. If we increase batch size, will it often get cheaper and faster?
A. In decode, batching can increase throughput. With mixed prefill and decode, TTFT and inter-token latency can change. KV cache growth can also limit concurrency through memory.

Q3. Isn’t quantization often worth using in the end?
A. It can reduce cost and latency. TensorRT notes quality can drop due to rounding and clamping error. That suggests defining an acceptable quality floor via evaluation.

Conclusion

For GPU inference optimization, decomposition often comes before techniques. Break latency into queue vs compute and prefill vs decode. Then tune batching, KV cache, scheduling, and compression with measurements.

Next, consider ways to increase concurrency with less KV cache waste. Paging is one reported approach. Tie compression decisions to an evaluation framework and observed bottlenecks.

Aionda

Decomposing LLM Inference Latency for Better Serving Performance

TL;DR

TL;DR

Current state

Analysis

Practical application

FAQ

Conclusion

Further Reading

References

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