What Performance Features Does vLLM Offer?

vLLM's performance features fall into four groups: caching (automatic prefix caching, KV cache management), decoding and context handling (speculative decoding, context extension), parallelism and deployment (data parallel, tensorize, disaggregated serving), and observability (metrics, profiling, tracing). Which ones matter to you depends on whether you are running offline batch inference or an online serving endpoint, and on whether your bottleneck is memory, latency, or throughput.

Caching and memory efficiency

Automatic prefix caching

When many requests share a common prefix — a long system prompt, a fixed instruction block, or a shared document — vLLM can reuse the computed KV cache for that prefix instead of recomputing it per request. This is listed under the Features section of the docs as Automatic Prefix Caching. The practical effect: repeated-prefix workloads (chatbots with a fixed persona prompt, RAG with a shared context header) spend less time in prefill.

KV cache control

The docs include Reset KV, KV Events, KV Load Failure Recovery Test, and Sharded State entries. These are relevant when you need to manage or observe the KV cache directly rather than just benefit from it implicitly — for example, resetting cache state between workloads or recovering when a cache load fails.

Pause and resume

Pause Resume lets you suspend and restart serving work. This matters for maintenance windows or when you need to free resources temporarily without tearing down the whole deployment.

Decoding and context handling

Speculative decoding

Listed as Speculative Decoding, this is the feature to look at when token generation latency is your constraint. It works by using a smaller draft model to propose tokens that the main model verifies, which can raise tokens-per-second on generation-heavy workloads.

Context extension

Context Extension addresses the case where your inputs exceed the model's native context window. If your workload involves long documents or long conversations, this is the relevant feature rather than a generic throughput optimization.

Structured output and logits control

Structured Outputs, Custom Logits Processors, and Structured reads on DiffusionGemma are grouped under Features. These constrain generation to a schema or modify logit processing. They are correctness features more than raw-speed features, but they matter for performance in the sense that they avoid wasted generation that gets discarded for not matching a format.

Parallelism and distributed deployment

The docs also cover LMCache Examples, Mooncake Connector, FlexKV Connector, and Nixl Connector under the Disaggregated section — these are connector options for moving KV data between stages or nodes. If you are evaluating disaggregated prefill/decode, those connectors are the concrete integration points to read next.

For RL and training-adjacent workloads, there is a dedicated RL section covering weight sourcing, async APIs, and several HTTP/IPC/NCCL/FSDP combinations. That is a different performance problem (weight transfer and rollout throughput) than inference serving.

Observability and profiling

You cannot tune what you cannot measure, and the docs provide:

• Monitoring Dashboards and Metrics Setup — for ongoing production visibility
• Prometheus and Grafana — the standard pairing for scraping and visualizing those metrics
• OpenTelemetry POC — for trace-based observability
• Profiling — for finding where time actually goes inside the engine
• Trace Replay — replaying recorded traces, useful for reproducing a performance issue
• Production Metrics — a dedicated page under General
• Reproducibility — for making benchmark results repeatable

If you are diagnosing a latency regression, the sequence the docs support is: enable metrics, look at dashboards, then profile, then replay the trace.

How to choose

• Shared prefixes across requests → Automatic Prefix Caching
• Generation latency too high → Speculative Decoding
• Inputs longer than the context window → Context Extension
• Need more throughput on one node → Data Parallel or Tensorize
• Need to scale past one node → Ray Serving / Multi-Node-Serving
• Want to tune prefill and decode separately → Disaggregated Serving plus a connector
• Don't know where the time goes → Metrics Setup, then Profiling

The docs also include a Frequently Asked Questions page and a Troubleshooting page under General, which are the right places to check when a feature does not behave as expected. Note that the documentation does not state pricing or login requirements for these features; treat availability as something to confirm against your own installation and the specific docs page for each feature.