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Neither engine is faster in every case. SGLang’s published advantages come from reusing shared prompt prefixes through RadixAttention, decoding constrained outputs with compressed finite-state machines, and running multi-call language-model programs in one runtime. vLLM’s central contribution is PagedAttention, which manages the KV cache in fixed-size blocks so more requests fit in GPU memory. Which engine is faster for your service depends on how much your prompts overlap, how strictly outputs are constrained, the exact versions you deploy, and whether you measure throughput or latency at the concurrency you actually run.
The head-to-head figures that exist come from papers published in 2023 and 2024, measured against engine versions that have since changed. They explain the designs well, but they cannot tell you which engine is faster today.
What each design is built around
SGLang: a runtime for structured LLM programs
The SGLang paper, by Lianmin Zheng and coauthors (NeurIPS 2024), describes two layers: a front end for composing multi-call language-model programs, and a back-end runtime that executes them. The authors summarize the runtime’s main optimizations as: “The runtime accelerates execution with novel optimizations like RadixAttention for KV cache reuse and compressed finite state machines for faster structured output decoding.”
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vLLM: paged KV-cache memory
The vLLM paper (2023) introduces PagedAttention. It divides each sequence’s KV cache into fixed-size blocks that do not have to sit in contiguous GPU memory. A cache manager allocates blocks as a sequence grows and releases them when the request finishes. The paper’s argument is that reducing fragmentation and redundant allocation lets more requests fit in memory, which supports larger batches and higher throughput.
That describes the original design. Current vLLM includes features the 2023 paper does not cover, so treat the paper as the origin of its memory model, not as a description of the release you would deploy.
Prefix reuse: where RadixAttention pays off
RadixAttention’s value depends on whether requests share prefixes. The clearest cases are:
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- Repeated system prompts that every request in a service carries.
- Few-shot examples prepended to many queries.
- Agent templates that share a long instruction block across calls.
- Chat histories where each new turn extends a conversation the engine has already processed.
When requests are largely unrelated, there is little prefix to reuse and the mechanism contributes little. Prefix overlap, not the engine name, decides whether this axis matters for your traffic, so measure the overlap in production logs before deciding it is important.
Structured decoding: compressed finite-state machines
When an output must follow a grammar, such as a JSON schema, the runtime can track which tokens are valid at each step. The SGLang paper represents the constraint as a finite-state machine and compresses adjacent edges that have only one possible transition.
The practical effect is easiest to see with a fixed key. Suppose a schema requires an object whose first key is "name". After the opening quote, the characters that spell the key and the separator are forced by the grammar. A runtime that recognizes this run of predetermined tokens can decode them in a single forward pass instead of advancing one token at a time. Free-text string values, where many tokens remain valid at every step, get no such shortcut. The savings therefore scale with how much of your output schema is fixed.
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This is the mechanism the paper describes and evaluates. It is not the only structured-decoding strategy in serving systems, and constrained-decoding backends and interfaces change between releases. Check the release notes and the decoding backend of the exact version you run before assuming that a constrained request takes the compressed path.
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What the published benchmark numbers measure
The two papers report the following figures. Each one is tied to a specific study and version, so the conditions column matters as much as the number.
| Reported result | Figure | Source | Conditions and limits |
|---|---|---|---|
| SGLang throughput versus compared systems | Up to 6.4× higher | SGLang paper, NeurIPS 2024 | Maximum across the paper’s evaluated workloads; not a typical gain. Model and hardware are specified in the paper’s evaluation section. |
| SGLang latency versus compared systems | Up to 3.7× lower | SGLang paper, NeurIPS 2024 | Maximum across evaluated workloads, using the latency metric defined in the paper. |
| SGLang cache hit rate in the paper’s benchmark suite | 50% to 99% | SGLang paper, NeurIPS 2024 | Varies by workload; high values appear where requests share prefixes. |
| SGLang cache-aware scheduler | Average of 96% of the optimal cache hit rate | SGLang paper, NeurIPS 2024 | Measured against the optimal hit rate within the paper’s benchmark suite. |
| vLLM throughput versus compared systems | 2–4× higher at similar latency | vLLM paper, 2023 | Historical evaluation against the systems of that period, not a comparison with current SGLang. |
The SGLang authors attribute their gains to KV-cache reuse, parallelism within a program, and faster constrained decoding. Their own breakdown is uneven: multi-turn workloads with short outputs benefited from prefix-time savings, while long-output workloads with little shared context saw little speedup, because decoding dominated the run time.
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Why the head-to-head result is not a current verdict
The SGLang paper’s comparison used an earlier vLLM version. A later vLLM release partially integrated RadixAttention as an optional, experimental feature. So the paper’s comparison describes an earlier vLLM configuration, not the vLLM you would deploy today, and a paper maximum is not a release-versus-release result.
Avoid treating the two designs as rival camps. They address different parts of the serving problem, and the question is which behaviors your exact version exposes and how they are enabled. Answer that question by testing the versions you plan to run.
How to run a fair high-concurrency comparison
Match the setup
- The same model weights, precision, and tokenizer settings for both engines.
- The same accelerator model, GPU count, and memory allocation.
- Pinned software versions, recorded exactly, for both engines and their dependencies.
- The same parallelism layout, maximum context length, and serving configuration options.
SGLang’s project repository lists NVIDIA H100 among supported hardware. That confirms support; it does not show that H100 is required, that it offers the best value, or that it is faster than other accelerators for your model.
Build production-like traffic
- Prompt and output length distributions drawn from your own logs, not a single synthetic length.
- The arrival pattern your service sees, including bursts, not only a steady request rate.
- A shared-prefix stream and a low-reuse stream, weighted to match your real mix.
- A separate stream with constrained (schema or grammar) outputs, if your service uses them.
Control the cache state
Do not compare a warm-cache run on one engine with a cold-cache run on the other. Run both engines with the same warm-up procedure, and report warm and cold results separately. A cache that has already seen your system prompt will make the first-token numbers look better than a fresh deployment will.
Run the test in this order
- Pin the exact engine versions and record every configuration setting that differs from defaults.
- Warm up each engine with the same request set, then run a cold pass for comparison.
- Sweep concurrency across the range your service must handle, holding each level long enough to reach steady state.
- Record throughput, time to first token, inter-token latency, error rate, GPU memory use, and the concurrency at which queueing or saturation begins.
- Repeat the sweep for shared-prefix, low-reuse, and constrained-output traffic.
- Choose the engine by the latency and error targets your service must meet at its target load. Peak batch throughput does not substitute for latency at that load.
Matching workload to design
The table below connects common workloads to what the published designs argue and what to measure first.
Quick Recap
| Workload | What the published design argues | Measure first |
|---|---|---|
| Shared system prompts, few-shot examples, or agent templates | Prefix reuse is the main argument for RadixAttention in SGLang. | Cache hit rate and time to first token |
| Multi-turn chats with short answers | The SGLang paper reports prefix-time savings for this shape of workload. | Time to first token at target concurrency |
| Long generations over largely unrelated prompts | The SGLang paper found little speedup when decoding dominated and sessions shared little. PagedAttention’s memory efficiency is the main argument for vLLM here. | Throughput, memory headroom, and inter-token latency |
| Strict JSON or grammar-constrained output | Compressed finite-state-machine decoding is the SGLang paper’s mechanism for faster constrained output. | Tokens per second with and without the constraint, on the exact deployed version |
(Continuing)
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