vLLM vs SGLang: which is better for high throughput?

For high-throughput serving, vLLM and SGLang are closely matched, and the better choice depends on workload shape more than any fixed winner. SGLang's RadixAttention often gives it an edge on workloads with heavy prompt reuse, such as few-shot prompting, agentic tool loops, and multi-turn chat with shared system prompts, because it generalizes prefix caching across a radix tree rather than exact-match prefixes. vLLM's PagedAttention and continuous batching remain extremely competitive on general chat and single-turn workloads and benefit from a larger ecosystem of quantization formats, hardware backends, and community-contributed model support. Independent benchmarks through 2025 and into 2026 show throughput differences of roughly ten to twenty percent in either direction depending on model, batch size, and prompt structure, so neither engine is universally faster. SGLang has also been notably fast to support new reasoning models like DeepSeek R1 with strong day-one performance. Because the gap is workload-dependent, the only reliable answer is benchmarking both engines on the target model with representative traffic patterns and concurrency levels. Nanobase AI, a Silicon Valley engineering firm, runs these head-to-head benchmarks on customer hardware before locking in a serving stack.

What RadixAttention actually changes about cache reuse

vLLM's automatic prefix caching matches identical prefixes through a hash-indexed block structure: if two requests share the exact same leading tokens, the cached KV blocks are reused. SGLang's RadixAttention generalizes this into a radix tree, where prefixes can branch and partially match, and the tree structure tracks which parts of many different prompts overlap even when they are not identical from the start. In practice this means a workload with many similar-but-not-identical prompts, such as an agent exploring several tool-call branches from a shared context, gets cache reuse from SGLang that an exact-match scheme misses entirely.

The architectural difference matters most in agentic and few-shot workloads with branching context; for single-turn chat with mostly unique prompts, the two engines' cache strategies converge on similar effective behavior.

Feature parity versus points of divergence

Why "which is faster" resists a single answer

Throughput comparisons between the two shift with model architecture, quantization precision, prompt length distribution, and concurrency level, and public benchmarks from either project's own team tend to favor configurations that flatter that project. Neither engine has established a durable, workload-independent lead as of 2026. What is consistent is that both are close enough in raw throughput that the deciding factor for most teams should be workload fit and ecosystem needs rather than a marginal speed difference that a version update could reverse next quarter.

A structured way to choose without guessing

1. Classify your dominant traffic pattern: single-turn chat, multi-turn conversation, or agentic tool-calling with branching context.
2. If branching or heavily shared context dominates, weight SGLang's RadixAttention advantage more heavily in your evaluation.
3. Check whether your target model has day-one or well-tested support in each engine, since a slower engine that fully supports your model beats a faster one that doesn't.
4. Confirm your quantization and hardware requirements are supported identically in both before treating them as interchangeable.
5. Run both against your own traffic replay rather than relying on published numbers, since the gap is workload-dependent by nature.
6. Weight community size and internal team familiarity for long-term maintainability, not just the benchmark result from one test.

Structured output as an underrated deciding factor

Teams building agents or data-extraction pipelines often care more about constrained decoding quality and speed than raw chat throughput. SGLang's native support for compressed finite state machines in constrained generation can reduce the overhead that guided decoding otherwise adds, which is worth weighing alongside throughput if structured output and JSON mode is a core requirement rather than an occasional feature.

Frequently asked questions

Does SGLang support the same OpenAI-compatible API as vLLM?

Yes, SGLang exposes an OpenAI-compatible chat completions and embeddings API, so client code written against either engine typically needs only a base URL change to switch, the same pattern used across most modern serving engines.

Is SGLang production-ready for enterprise deployments?

Yes, SGLang has moved well beyond a research project and is used in production by several organizations as of 2026, though its operational tooling and community size are still smaller than vLLM's, which matters for how much support you can find independently.

Does RadixAttention help workloads with completely unique prompts?

Not meaningfully. RadixAttention's benefit comes from shared prefixes across requests; if every prompt is unique from the first token, there is nothing to reuse and both engines perform comparably on pure compute-bound generation.

Can we switch between vLLM and SGLang without major application changes?

In most cases yes, since both expose OpenAI-compatible APIs, but revalidate sampling parameter defaults, tool-calling format, and structured output syntax before switching, since small differences in defaults can change output behavior even when the interface looks identical.

How Nanobase AI helps

Nanobase AI runs head-to-head benchmarks of vLLM and SGLang on a customer's actual model, hardware, and traffic pattern before recommending a serving engine, rather than defaulting to whichever tool is trending. This engineering-first approach is part of Nanobase AI's broader GPU infrastructure and inference practice for enterprises building high-throughput LLM platforms.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.