vLLM vs SGLang: Choosing an LLM Inference Framework in 2026
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Vote nowServing large language models at production scale boils down to one problem: getting the most tokens out of your GPU per second, per dollar. Two open-source frameworks dominate this space in 2026. vLLM, the established heavyweight from UC Berkeley, holds roughly three times the GitHub stars of SGLang, the challenger from the same research lineage that has carved out a reputation for speed and developer ergonomics. Both are serious engineering projects with backing from major labs, and both can handle the workloads that power real products. The question is not which one is better in absolute terms, but which one fits your deployment, your hardware, and your latency budget.
| vLLM | SGLang | |
|---|---|---|
| GitHub Stars | ~77,000 | ~26,000 |
| Latest Version | v0.19 | v0.5.10 |
| License | Apache 2.0 | Apache 2.0 |
| Primary Language | Python / CUDA | Python / CUDA |
| First Release | June 2023 | June 2024 |
| Repository | github.com/vllm-project/vllm | github.com/sgl-project/sglang |
| Documentation | docs.vllm.ai | docs.sglang.io |
vLLM has the larger community, more integrations, and broader hardware support. SGLang is younger but has shipped aggressive optimisations at a pace that has made it the default choice in several AI labs and benchmark leaderboards.
The core technical difference between the two frameworks lies in how they manage the KV cache, the working memory that grows with every token generated during inference.
vLLM introduced PagedAttention, which eliminates memory fragmentation by borrowing a concept from operating system virtual memory. Instead of allocating a contiguous block of memory for each request's KV cache, vLLM divides the cache into fixed-size pages and maps them into logical blocks. When a request finishes, its pages return to a free pool that any new request can reuse, regardless of size.
This approach prevents the classic problem where pre-allocation wastes GPU memory (allocating for the maximum sequence length) while dynamic allocation causes fragmentation. PagedAttention also enables continuous batching, where requests enter and leave the batch at different times without stalling the GPU.
SGLang builds its approach on top of the same paged KV cache allocation concept, then adds a radix tree data structure on top. RadixAttention stores computed KV cache blocks keyed by their token prefix. When a new request arrives, SGLang checks whether any previously computed request shares a common prefix. If it does, the framework reuses those cached blocks instead of recomputing them.
This is not a replacement for paged allocation. It is a layer above it. The paged allocator still handles the physical memory management. The radix tree handles the logical reuse of already-computed attention states across requests. For applications with repeated prompts, system prompts, or few-shot examples, this prefix caching can eliminate a significant fraction of redundant computation.
Getting LLMs to produce valid JSON, XML, or custom grammars is a production requirement, not a nice-to-have.
SGLang defaults to xgrammar, a Rust-based constraint engine that generates token masks on the CPU and overlaps that mask generation with GPU computation. This overlap means the grammar checking adds negligible latency. The integration is tight: you pass a schema and the framework handles the rest.
vLLM supports structured outputs through outlines, xgrammar, and guidance as backend options. The flexibility is useful if you have an existing pipeline built on one of these libraries, but the default experience is less turnkey than SGLang's. The performance difference is marginal for most workloads, though SGLang's overlapped mask generation has an edge at low batch sizes where every millisecond counts.
Speculative decoding uses a small draft model to predict multiple tokens ahead, then verifies them in a single forward pass through the large target model. When the draft guesses correctly, you get multiple tokens for the cost of one. Both frameworks support this technique.
Both support EAGLE, Medusa-style multi-token prediction (MTP), n-gram, and custom draft models. The key differentiators in 2026 are:
SGLang ships EAGLE-3, which delivers roughly 2.36x speedup on standard benchmarks and is the recommended approach. SGLang also provides SpecForge, a tool for training custom EAGLE-3 draft models tailored to your specific target model and workload.
vLLM added P-EAGLE in March 2026, a parallel variant that runs the draft model and verification concurrently rather than sequentially. This is particularly effective on multi-GPU setups where the draft model can occupy a separate GPU from the target model.
A notable performance gap appears under heavy concurrency. At 150 or more concurrent requests, SGLang hits Python's Global Interpreter Lock (GIL) limits. Benchmarks on NVIDIA L4 GPUs show SGLang plateauing around 150 requests per second, while vLLM reaches 364 requests per second. CPU utilisation figures tell the story: SGLang sits at roughly 127% (GIL-bound, unable to use more cores), while vLLM hits 251% (exploiting parallelism more effectively).
This matters for high-throughput serving scenarios. If you are running a public API handling thousands of concurrent users, vLLM's concurrency ceiling is a genuine advantage. For moderate loads or single-user inference, the difference is irrelevant.
vLLM supports the widest range of accelerators: NVIDIA GPUs, AMD ROCm, Intel Gaudi, Google TPU, and AWS Trainium. If your deployment spans multiple cloud providers or uses non-NVIDIA hardware, vLLM is the safer bet.
SGLang's hardware support is narrower but covers the major use cases: NVIDIA and AMD GPUs, Apple Silicon via MLX, Huawei Ascend NPUs, and Ollama for local development. The Apple Silicon support is a differentiator if you develop or test on Mac hardware before deploying to GPU clusters.
Both projects have shipped significant releases in early 2026.
vLLM v0.19 brought day-0 support for Gemma 4, MORI-IO for improved memory-over-RDMA networking between GPUs, and support for NVIDIA B300 accelerators. The Gemma 4 support matters because it demonstrates vLLM's ability to integrate new models quickly, often within hours of a model's public release.
SGLang v0.5.10 introduced Elastic EP, which provides partial failure tolerance for expert parallelism in mixture-of-experts models. If one expert node fails, the serving job continues with degraded performance rather than going down entirely. This release also made Piecewise CUDA Graph the default execution mode and added Flash Attention 4 support.
The StableLearn benchmark provides a useful reference point. Running Llama-3-70B on an A100 80GB GPU, the results are:
The margins are tight. SGLang edges ahead on both metrics, but the difference is unlikely to drive a deployment decision on its own. The real deciding factors tend to be hardware compatibility, concurrency requirements, and developer experience.
| Choose vLLM when... | Choose SGLang when... |
|---|---|
| You need the widest hardware support (AMD, Gaudi, TPU, Trainium) | You want the fastest single-node throughput |
| You serve 150+ concurrent requests | You use repetitive prompts or few-shot examples (RadixAttention helps) |
| You need day-0 support for new model releases | You want structured outputs that work out of the box with xgrammar |
| You run multi-GPU serving across different accelerator types | You develop on Apple Silicon via MLX |
| You need production-hardened concurrency management | You want EAGLE-3 speculative decoding with SpecForge training |
| Your team already depends on outlines or guidance for constrained generation | You need Elastic EP for MoE fault tolerance |
Both frameworks are converging on the same set of capabilities while differentiating on execution. vLLM's breadth of hardware support and concurrency scaling make it the default choice for large-scale deployments with diverse infrastructure. SGLang's architectural innovations, particularly RadixAttention and EAGLE-3, give it an edge in single-node throughput and developer productivity.
The practical reality is that both are mature, well-maintained projects. The cost of switching between them is low because they both implement the OpenAI-compatible serving API. A sensible approach is to benchmark your specific model and workload on both, then pick whichever gives you better numbers on your hardware. For new projects, SGLang's defaults are easier to get right. For infrastructure that needs to run everywhere, vLLM's reach is unmatched.