SGLang vs vLLM: How to Choose the Right Engine for Production LLM Serving

Teams that self-host open-weight models usually end up choosing between two open source serving engines. The SGLang vs vLLM decision shapes response latency, GPU spend and how much tuning your platform team owns after launch.

The stakes keep rising. Data shared by Vercel CEO Guillermo Rauch shows open-weight models processed almost 80% of production tokens on Vercel’s AI Gateway in September 2026. Every one of those tokens passes through an LLM inference engine that decides how requests are batched, where cached state lives and how GPU memory is spent.

This guide explains how each engine works, why published benchmarks contradict each other, and how to make the call using your own traffic instead of someone else’s test set.

SGLang or vLLM: which should you use? Choose SGLang when a large share of requests reuse the same prefix, such as system prompts, tool definitions, RAG context or multi-turn history, because RadixAttention skips recomputing it. Choose vLLM for broader hardware and model coverage, deeper documentation and simpler operations. On unique prompts, the two perform comparably.

Key Takeaways

• Both engines solve the same core problem: managing the KV cache efficiently on GPUs.
• vLLM’s PagedAttention removed the 60–80% KV cache memory waste found in earlier serving systems. Paged KV management is now standard in both engines.
• SGLang’s RadixAttention reuses cached prefixes across related requests, which matters most for agents, RAG and chat.
• Independent tests from Modal found the engines comparable on unique prompts, a setup that deliberately left out prefix reuse.
• vLLM leads on hardware breadth, documentation depth and foundation governance status.
• The benchmark that decides it is your own traffic. Measure prefix overlap before anything else.

What an LLM Inference Engine Actually Does

Every LLM request runs in two phases. In prefill, the model processes the whole prompt at once and computes attention keys and values for every input token. In decode, it generates output one token at a time, reusing those stored keys and values.

That stored state is the KV cache. It saves the model from repeating the same math for every new token, but it grows with context length and conversation history and can consume most of a GPU’s free memory. Workloads also stress the phases differently: long-document summarization is prefill-heavy, while code generation with long outputs is decode-heavy.

An inference engine is, at its core, a scheduler and memory manager for the KV cache. It decides which requests share a batch, where cached state lives, what gets evicted and how to keep the GPU busy as requests arrive and finish. The model weights are identical in both engines; the throughput and latency differences come from these scheduling and memory decisions.

vLLM Explained

vLLM came out of UC Berkeley in 2023 with one target: wasted GPU memory. Because a conversation’s cache keeps growing and output length is unknown, older serving systems reserved large contiguous memory blocks up front. The original vLLM paper found those systems wasted 60–80% of KV cache memory, which capped how many requests a GPU could serve at once.

PagedAttention fixed this by splitting the KV cache into small fixed-size blocks that can sit anywhere in GPU memory, much like operating-system memory pages. Blocks are allocated as a sequence grows and released when the request ends, so the same GPU holds far more concurrent requests.

Automatic Prefix Caching extends this by reusing KV blocks when requests begin with identical tokens, such as a shared system prompt. It is controlled by the --enable-prefix-caching flag, and its default has changed across releases, so confirm it for the version you deploy.

Around that core, vLLM has grown into a broad serving ecosystem that aims to run any model on any accelerator and any cloud. Red Hat is its largest corporate contributor, and the project publishes recipes with ready-to-run commands for specific models on specific hardware.

SGLang Explained

SGLang (Structured Generation Language) was introduced through LMSYS in January 2024. It targets applications that make many related model calls: multi-turn chat, extraction pipelines and agents that branch and loop.

Those applications repeat themselves constantly. An agent sends the same system prompt and tool definitions on every call, plus a conversation history that only grows. RadixAttention stores computed KV cache in a radix tree keyed by token prefix. A new request walks the tree, reuses the longest matching branch and computes only the new tokens. Branches that go unused longest are evicted first.

SGLang pairs this with cache-aware scheduling, which runs requests sharing a prefix close together so the cached branch is still resident when they need it. For structured generation, it uses the xgrammar backend by default and reuses compiled grammars across requests with the same JSON schema, which keeps per-request overhead low for repeated tool schemas.

One common misconception is worth correcting. You do not need to learn a custom language to serve models with SGLang. Its server exposes a standard OpenAI-compatible API, and its frontend language is optional for teams scripting complex generation workflows.

Also Read: AI Data Security Platform

Key Difference Between vLLM and SGLang

The two projects began with different answers to the same KV cache problem. vLLM optimized how KV memory is allocated, so more requests fit on one GPU. SGLang optimized how KV work is reused across related requests, so repeated context is not computed twice.

Each engine has since adopted the other’s core idea, but the depth differs. vLLM’s prefix caching reuses blocks when prompts start with identical tokens. SGLang’s radix tree handles arbitrary prefix boundaries and branching conversation histories, and its scheduler orders requests to maximize cache hits. When requests share almost nothing, the difference largely disappears.

What SGLang and vLLM Now Share

Both engines now ship most of the same production features:

• Continuous batching keeps the GPU busy as requests arrive and finish.
• Prefix caching skips recomputing shared prompt instructions.
• Chunked prefill splits long prompts into smaller pieces scheduled alongside other work.
• Structured outputs constrain generation to JSON or a grammar, with xgrammar as the default backend in both.
• Speculative decoding proposes and verifies candidate tokens to speed up decode.
• Quantization (such as FP8) cuts memory use and can raise throughput.
• Distributed execution spreads large and Mixture-of-Experts models across GPUs with tensor and expert parallelism.

Both expose OpenAI-compatible endpoints, so application code rarely changes when you switch. Both typically offer day-of-release support for major new models, and both treat NVIDIA and AMD GPUs as first-class targets.

SGLang vs vLLM: Head-to-Head Comparison

Dimension	vLLM	SGLang
Origin	UC Berkeley, 2023	LMSYS, January 2024
Core memory design	PagedAttention: KV cache in on-demand fixed-size blocks	RadixAttention: KV blocks in a radix tree indexed by token prefix
Prefix reuse	Automatic Prefix Caching for identical leading tokens	Arbitrary prefix boundaries and branching histories, plus cache-aware scheduling
Throughput on unique prompts	Comparable in Modal’s independent tests	Comparable in Modal’s independent tests
Best-fit traffic	Mixed or unique prompts, high-volume batch	Prefix-heavy chat, agents and RAG
Structured output	Constrained decoding with selectable grammar backends	Constrained decoding with compiled-grammar reuse across requests
Hardware	NVIDIA and AMD first-class; longer-documented support for TPU, Intel GPU, CPU and Apple	NVIDIA and AMD first-class; Intel GPU, TPU and more with a shorter track record
API	OpenAI-compatible	OpenAI-compatible
Governance	PyTorch Foundation-Hosted Project	PyTorch Foundation Ecosystem Project with independent governance
Model launch guides	vLLM recipes	SGLang cookbook

Sources: vLLM and SGLang project documentation, Modal LLM Engineer’s Almanac, PyTorch Foundation.

Why SGLang vs vLLM Benchmarks Disagree

Published comparisons range from “vLLM is several times faster” to “they are identical” to “SGLang cuts latency by a third.” Most of these results are accurate for what they measured. They disagree because they measured different things.

1. Workload shape and prefix overlap: Modal’s LLM Engineer’s Almanac tested both engines out of the box, with prompts built from random chunks of Pride and Prejudice, so very few requests shared a prefix. On Llama 3.1 8B on a single H100, Modal measured the highest request rate each engine sustained with p95 time to first token (TTFT) under one second:

Tokens in / out	Closest use case	vLLM (req/s)	SGLang (req/s)	vLLM p95 TTFT	SGLang p95 TTFT
1,024 / 128	RAG, summarization	17.08	16.88	0.52 s	0.83 s
128 / 1,024	Code generation	5.34	6.50	0.82 s	0.87 s
2,048 / 2,048	Long chat	1.23	1.25	0.65 s	0.74 s

Source: Modal LLM Engineer’s Almanac, out-of-the-box settings, single replica.

What this test cannot show is prefix reuse. Agents, RAG pipelines and chat apps resend the same system prompt, tool definitions or documents on every call. That is exactly the traffic RadixAttention and vLLM’s prefix caching are built for. A test built on shared prefixes can produce the opposite headline from the same engines on the same GPU.

2. Version drift: Both projects ship frequently. Older comparisons still in circulation describe a large vLLM throughput lead from before SGLang adopted paged memory management. Blackwell kernels and speculative decoding support change from release to release. A benchmark without version numbers has a short shelf life.

3. Default versus tuned configurations: Modal deliberately used default settings. Whether vLLM’s prefix caching is on, the memory fraction, maximum concurrent sequences and chunked prefill size can each shift results. A comparison that tunes one engine and not the other is not a comparison.

4. Single replica versus fleet: Nearly all published numbers come from one replica. In production, a load balancer decides whether a follow-up request lands on the replica that holds its cached prefix. Without cache-aware routing, a fleet can lose much of the prefix-reuse gain a single-node benchmark shows.

5. The metric that headlines the chart: Modal’s data shows vLLM with lower p95 TTFT on the RAG-shaped workload, and SGLang with higher throughput on the code-generation shape. Pick the metric your SLO is written in, not the one a vendor chose.

SGLang or vLLM? Choosing by Workload

Workload	Lean toward	Why
RAG with repeated document or instruction context	SGLang	Shared context is cached once and reused across queries
Multi-turn agents and tool calling	SGLang	System prompt, tool definitions and history are reused every turn
Structured JSON with repeated schemas	SGLang (slight)	Compiled grammars are reused across requests with the same schema
Batch jobs with unique prompts	Either	Throughput is comparable; decide on operations and hardware
Mixture-of-Experts models	Either; benchmark	Both support expert parallelism; results depend heavily on configuration
New GPU generations (e.g., Blackwell)	Either; check release notes	Kernel support changes release to release; validate on your exact SKU
Small platform team, broad model mix	vLLM	Wider documentation, recipes and hardware coverage
Need for a commercially supported distribution	vLLM	More enterprise platforms package and support it today

The gain from prefix reuse grows with the share of requests that overlap and with the length of the shared prefix. At low overlap it is hard to see; when most requests share a long prefix, it often becomes the deciding factor. Measure your own ratio rather than borrowing someone else’s threshold.

How to Benchmark Both Engines on Your Own Traffic

1. Sample real traffic. Export a representative window of production prompts, anonymized, including system prompts and tool definitions. Synthetic prompts hide prefix overlap.
2. Measure prefix overlap first. Tokenize the sample with your model’s tokenizer and calculate the share of requests whose first 256, 512 and 1,024 tokens match another request. This single number predicts most of the result.
3. Pin everything. Use the same GPU type, model, precision and container versions for both engines, and record them alongside the results.
4. Configure fairly. Enable prefix caching on vLLM, match memory fraction and context length, and warm up both engines before measuring.
5. Replay at realistic concurrency. Step through the concurrency levels you expect at average and peak load.
6. Track the right metrics:
   • TTFT at p50 and p95
   • Inter-token latency
   • Output tokens per second
   • Requests per second sustained within your latency SLO
   • Cache hit rate and GPU memory use (both engines expose a metrics endpoint)
7. Convert to cost. Cost per million tokens = (hourly GPU rate ÷ 3,600) ÷ (tokens per second ÷ 1,000,000).
8. Test failure modes. Include long-context spikes, cold caches after a restart and a model version upgrade.

GuideLLM, maintained under the vLLM project, can replay realistic prompts against any OpenAI-compatible endpoint, including SGLang, so one harness covers both engines.

Running SGLang and vLLM Together: Hybrid Routing Architecture

Many organizations do not have one traffic shape. A customer-facing assistant is prefix-heavy, while a nightly document-classification job sends unique prompts. Since both engines speak the same OpenAI-compatible API, you can run one pool of each behind a single gateway.

A practical layout looks like this:

• Gateway: LiteLLM or a lightweight custom proxy accepts every request and routes it by model alias, header or workload tag.
• SGLang pool: handles chat, agent and RAG traffic, where prefix reuse pays off.
• vLLM pool: handles batch and unique-prompt jobs, plus models and hardware your team already runs on vLLM.
• Session affinity: the gateway pins a conversation or agent run to the replica holding its cached prefix.
• Shared foundations: both pools load the same weights from shared object storage and report to the same Prometheus and Grafana stack.

The trade-off is operational. Two engines mean two upgrade cadences, two sets of flags and two security patch streams. Run both only when your traffic mix is genuinely split; otherwise, standardize on one. At larger scale, disaggregated serving layers such as NVIDIA Dynamo can sit above either engine and split prefill and decode onto separate worker pools.

Also Read: AI Development Cost

Enterprise Considerations Before Choosing an LLM Inference Engine

Raw performance is only part of the decision for a production platform. Five other factors usually matter more over a three-year horizon.

Governance and project maturity: In 2025, both projects joined the PyTorch Foundation ecosystem. vLLM is a Foundation-Hosted Project, which means it passed the foundation’s production-use and governance checks. SGLang is an Ecosystem Project that keeps its own independent governance. Both ranked among the fastest-growing open source projects by contributors in GitHub’s 2025 Octoverse report, with vLLM at third and SGLang at seventh.

Commercial support: vLLM is packaged and supported in several commercial enterprise AI platforms, which matters for teams that need a support contract. For SGLang, confirm what your cloud or platform vendor will support before committing.

Hardware roadmap: If Blackwell GPUs are in your next procurement cycle, check both engines’ latest release notes for kernel support on your exact SKU, since it changes from release to release. If you run AMD Instinct GPUs, both treat AMD as first-class, but benchmark both on your exact cards.

Talent and skills: vLLM has a deeper base of documentation and practitioners, which shortens onboarding. SGLang skills are growing fast but are rarer in the hiring market. Teams without in-house serving expertise can hire AI developers experienced with both engines.

Total cost of ownership: GPU rental is the visible cost. Engineer time for tuning and upgrades, observability, security patching and the duplicated effort of a two-engine setup often matter as much. On the savings side, a high prefix cache hit rate lowers cost per token directly. An AI consulting engagement can model these costs against your actual traffic before you commit infrastructure.

How Xicom Helps

Xicom’s engineers help teams choose, deploy and operate the serving layer for open-weight models. We benchmark both engines on your real traffic, design the routing and observability stack, and deploy models in your own cloud or on-premise. Our LLM development services cover the path from model selection to production serving. Through our AI model fine-tuning services, we adapt open-weight models to your domain, and you own the trained weights and adapters. With 20+ years of delivery and 1,800+ projects across 50+ countries, we take inference from proof of concept to production.

Conclusion

The SGLang vs vLLM question is less about which engine is faster and more about which one fits your traffic. vLLM is the safer default for broad model and hardware coverage, enterprise support and the simplest operations. SGLang earns its place when most requests share context, as in agents, RAG and multi-turn chat. Measure prefix overlap, benchmark on your own prompts and let that data decide.

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