To make an LLM serve faster and handle more users, measure its performance on representative traffic, then tune request batching, KV-cache memory, precision, and parallelism against explicit latency, quality, and cost targets. There is no universally fastest serving setup: results depend on the model, accelerator, runtime, request lengths, and concurrency.
What to measure before tuning
Define the workload before changing the serving stack. Record the model and precision, prompt and output-length distributions, expected concurrency, streaming behavior, service-level objectives (SLOs), and target hardware. A benchmark with short prompts and a benchmark with long contexts may stress very different parts of the system.
Capture a baseline using the same request mix and hardware you plan to evaluate. Track these metrics together rather than relying on a single throughput number:
- Time to first token (TTFT): how long a user waits before generation begins.
- Time per output token: the pace of generation after the first token.
- End-to-end latency: total time to complete a request, including prompt processing and generation.
- Throughput at stated concurrency: requests or tokens served over time under a specified number of simultaneous requests.
- GPU memory and headroom: memory used during the test and the capacity left for variation in traffic or context length.
- Output quality and error rate: whether a faster configuration still meets task-quality requirements and serves requests reliably.
- Cost per request, startup time, and operational complexity: factors that can make a nominally faster setup a worse production choice.
For multi-GPU or multi-node configurations, also measure interconnect bandwidth, synchronization overhead, scaling efficiency, and recovery behavior. Report the model, hardware, runtime version, driver and CUDA stack, request mix, concurrency, and measurement method with every benchmark. Without those details, a speedup figure is difficult to interpret or reproduce.
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Tune request scheduling and batching
Use continuous or in-flight batching
Batching lets the serving system process work for multiple requests together. Continuous, also called in-flight, batching can add new requests as others finish instead of waiting for every request in a fixed batch to complete. That helps keep the GPU supplied with work as traffic arrives and can improve utilization.
Batch size and scheduling still need to fit the latency target. Larger or more aggressive batches can improve throughput, but may increase waiting time before a request is processed. Evaluate throughput and latency at the concurrency and request-length mix your service actually expects; do not select a setting solely because it maximizes tokens per second.
vLLM documents continuous batching and chunked prefill, while NVIDIA documents in-flight batching in TensorRT-LLM. These are capabilities, not guarantees of a particular speedup: the benefit depends on the model, hardware, runtime, and traffic pattern.
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Manage KV-cache memory to increase concurrency
During generation, the key-value (KV) cache stores information needed to continue each active sequence. Its memory use grows with active requests and context length, so it is a central limit on how many requests can run concurrently. A configuration that fits a few short requests may run out of room with more simultaneous users or longer prompts.
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Set memory limits from measured demand
In vLLM, KV-cache sizing affects batch concurrency and throughput. Its optimization guidance warns that conservative sizing can cap concurrency, while optimistic sizing can cause allocation failures. Test cache settings with representative and demanding request lengths, and watch both memory headroom and request failures; maximizing the configured cache is not a substitute for validating that allocations succeed.
Consider paged attention, prefix caching, and chunked prefill
Paged attention is a memory-management approach used by serving systems to manage KV-cache blocks. Prefix caching can reuse work for shared prompt prefixes where the runtime and workload support it. Chunked prefill breaks prompt processing into smaller pieces, which can help schedule prompt processing alongside ongoing generation. These features address different aspects of memory use and scheduling, so measure their effects separately where practical.
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vLLM lists paged attention, prefix caching, and chunked prefill among its serving capabilities. The useful settings depend on the model and request mix; shared prefixes, long prompts, and many active sequences do not affect every service in the same way.
Evaluate quantization against quality and hardware support
Quantization represents model weights or other values at lower precision to reduce memory pressure and potentially improve inference performance. The trade-off is that output quality can change, and supported formats and performance depend on the model, runtime, and accelerator. A format that is available in a serving engine is not automatically the best choice for a particular deployment.
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Choose parallelism when one GPU is not enough
Parallelism distributes computation, model components, or memory across devices. It can make a large model serve on available hardware or improve capacity, but adds communication, synchronization, and scheduling costs. Measure those costs rather than assuming that adding GPUs will produce proportional gains.
| Approach | What it distributes | What to evaluate |
|---|---|---|
| Tensor parallelism | Computation within model layers across devices. | Interconnect bandwidth and synchronization overhead, as well as latency and throughput. |
| Pipeline parallelism | Model stages across devices. | Pipeline scheduling, stage balance, and the effect on end-to-end latency and utilization. |
| Expert parallelism | Expert components in models and runtimes that support this arrangement. | Communication and scheduling behavior for the model and traffic pattern. |
| Context parallelism | Context processing across devices where supported. | Memory and communication costs for the target context lengths. |
vLLM’s distributed-inference guidance covers tensor and pipeline parallelism, pipeline scheduling, expert parallelism, and quantization as scalable-serving tools. Its documentation also lists data and context parallelism. Which combinations are supported and useful depends on the model and runtime; benchmark each design against a single-device baseline when that baseline is feasible.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Select a serving runtime by workload, not reputation
vLLM, TensorRT-LLM, TGI, and other inference engines expose overlapping techniques, including batching, memory optimization, and quantization. The best result depends on the accelerator, model architecture, precision, traffic pattern, and SLO. Compare candidates on the same hardware and request mix, including startup time and operational complexity as well as steady-state latency, throughput, memory, quality, and cost.
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NVIDIA describes TensorRT-LLM as providing streaming, in-flight batching, paged attention, quantization, and Triton integration for GPU inference. Google Cloud’s GKE guidance recommends evaluating quantization, tensor parallelism, and memory optimization for GPU-backed vLLM or TGI deployments. Those documented capabilities and recommendations are useful starting points, not a substitute for measuring the configuration you intend to run.
Move to Kubernetes or multiple nodes when the need justifies it
Kubernetes can help deploy and scale serving workloads, and multi-node serving can make additional capacity or model placement possible. Neither removes the need to tune the model, runtime, hardware, and workload. Distributed deployment also adds operational concerns such as scheduling, networking, synchronization, and failure recovery.
Use Kubernetes or a multi-node design when capacity, availability, or model size justifies that added operational cost. vLLM documents Kubernetes deployment patterns and gRPC examples; Google Cloud provides GKE guidance for GPU-backed vLLM and TGI deployments. Validate scaling behavior and recovery under your own service requirements rather than treating a deployment pattern as a performance benchmark.
A practical optimization sequence
- Define the test workload. Specify the model and precision, prompt and output-length distributions, concurrency, streaming behavior, SLOs, and target hardware.
- Measure a baseline. Record TTFT, time per output token, end-to-end latency, throughput at stated concurrency, GPU memory, errors, quality, and cost.
- Tune scheduling and batching. Evaluate continuous or in-flight batching at concurrency levels that matter to the service, checking its effect on both throughput and latency.
- Tune memory management. Test KV-cache limits, prefix caching, chunked prefill, and memory utilization with representative context lengths. Track allocation failures and remaining headroom.
- Test quantization. Compare supported precisions on target hardware and accept a candidate only if it meets the quality and performance criteria you set.
- Evaluate parallelism. Start with tensor or pipeline parallelism when appropriate, then assess expert or context parallelism if the model and serving runtime support them. Include communication and scheduling overhead in the comparison.
- Scale the deployment topology if needed. Adopt Kubernetes or multi-node serving when capacity, availability, or model size warrants the additional operational work.
- Publish reproducible results. State the exact model, hardware, runtime version, driver and CUDA stack, request mix, concurrency, and measurement method for each benchmark.
How to interpret performance claims
There is no single safe speedup number for LLM inference across models, hardware, sequence lengths, concurrency, and runtime versions. Official documentation describes capabilities and configuration effects, but a result from one setup does not establish what another setup will achieve. Treat every claimed speedup as workload-specific, and compare configurations using the same reproducible test and acceptance criteria.
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