The SGLang vs vLLM decision comes down to focus. Both engines serve open-weight LLMs through an OpenAI-compatible API, and both use continuous batching. SGLang optimizes for workloads that reuse prompt prefixes, such as agents, multi-turn chat and structured output. vLLM covers the widest range of models, hardware and integrations, and it has the larger community. Both run well on a single GPU VPS, so your workload decides the winner.
How SGLang and vLLM Differ
Both projects solve the same core problem: keeping a GPU busy while many users send requests at once. vLLM started at UC Berkeley’s Sky Computing Lab, and the PyTorch Foundation now hosts it under the Linux Foundation. SGLang grew out of the LMSYS research team. Their features overlap more with every release, but each engine still leans in its own direction. The table below compares them at a conceptual level.
| Aspect | SGLang | vLLM |
| Core memory technique | RadixAttention, a tree of cached prefixes shared across requests | PagedAttention, which stores the KV cache in fixed-size blocks |
| Prefix caching | Central to the design, with automatic matching of shared prefixes | Automatic prefix caching added on top of hashed KV blocks |
| Scheduling | Cache-aware scheduler that groups requests with shared prefixes | Continuous batching with chunked prefill, tuned for steady throughput |
| Structured output | Mature constrained decoding, plus jump-forward decoding for fixed text | Constrained decoding through grammar backends such as XGrammar |
| Programming model | OpenAI-compatible API plus a Python frontend language for multi-step prompts | OpenAI-compatible API plus an offline Python API for batch jobs |
| Typical use case | Agents, multi-turn chat, RAG with shared templates, structured extraction | General-purpose serving, wide model mix, multi-tenant platforms |
| Ecosystem maturity | Fast-growing, with strong adoption in large production deployments | Larger community and broad integrations with Kubernetes, Ray and cloud platforms |
| Hardware coverage | NVIDIA, AMD, Intel Xeon, TPU, Ascend | NVIDIA, AMD, Intel, TPU, CPU, plus plugins for more accelerators |
| License | Apache 2.0 | Apache 2.0 |
Prefix Caching and Scheduling
The biggest technical difference lies in how each engine reuses work. A model stores intermediate results for every token in its KV cache. When two requests share the same opening tokens, an engine can reuse that cache instead of computing it twice. SGLang built its whole design around this idea. RadixAttention keeps cached prefixes in a tree, and the scheduler orders requests to maximize cache hits. Long system prompts, chat histories and agent loops benefit the most.
vLLM started from a different angle. PagedAttention splits the KV cache into fixed-size blocks, much like virtual memory in an operating system. This approach reduces memory waste and fits more requests onto the GPU at once. vLLM later added automatic prefix caching on top of these blocks. Today both engines reuse shared prefixes. SGLang still tends to extract more from heavy prefix reuse. vLLM delivers strong, predictable throughput across mixed traffic.
Benchmarks shift with every release, model and configuration. Treat published numbers as a starting point, and test with your own prompts before you decide.
Ecosystem, Hardware and Maturity
vLLM has the larger community and the longer track record. New open-weight models often run on vLLM on release day. Cloud platforms, Kubernetes operators and frameworks like Ray ship ready-made vLLM integrations. Its documentation covers a wide range of setups, and many hosted AI services run on it behind the scenes.
SGLang moves fast and has caught up in many areas. Several large AI companies run it in production for high-traffic deployments. Its feature set targets demanding scenarios such as large mixture-of-experts models and long agent sessions. The trade-off is a smaller pool of tutorials and third-party tooling. Teams sometimes need to read source code or GitHub issues to solve edge cases.
Hardware support overlaps heavily. Both engines target NVIDIA GPUs first and also run on AMD GPUs through ROCm. vLLM reaches more accelerator types through its plugin system. SGLang supports a slightly narrower but growing list of platforms.
Which One Fits Your Workload
Neither engine wins every case. The right pick depends on your traffic pattern, your models and how much tuning your team wants to do.
When SGLang Fits Better
SGLang suits workloads where requests repeat large parts of their input. Agent frameworks are the clearest example. An agent sends the same instructions and tool definitions on every step, often dozens of times per task. Multi-turn chat assistants resend the full conversation with each message. RAG pipelines wrap retrieved text in fixed templates. In all these cases, RadixAttention turns repeated tokens into cache hits.
SGLang also fits teams that rely on structured output. If your application expects valid JSON in every response, its constrained decoding keeps output parseable without retry loops. Teams that build multi-step prompt programs gain from the frontend language as well.
Choose SGLang if your team is comfortable with a fast-moving project. Pin versions, read the release notes and test upgrades before you roll them out.
When vLLM Fits Better
vLLM suits teams that value breadth and stability. If you plan to try many different models, vLLM is the safer bet for day-one support. Platforms that serve several models or LoRA adapters to many users also benefit from its mature multi-tenant features.
vLLM also wins when integrations matter. If your stack already uses Kubernetes, Ray or a managed AI platform, ready-made vLLM support saves setup time. Its larger community means more guides, more answered questions and faster help when something breaks.
Pick vLLM as your default when your traffic is mixed and prefix reuse is low. Short, unrelated requests gain little from RadixAttention, and vLLM handles that pattern very well.
Running Either Engine on a Contabo GPU VPS
Both engines need a capable NVIDIA GPU with enough VRAM for model weights and the KV cache. Contabo’s GPU VPS fits that profile with a dedicated NVIDIA RTX PRO 6000 Blackwell Server Edition and 96 GB of GDDR7 ECC VRAM. The full card reaches your server through 1:1 PCI passthrough, without vGPU slicing. Each instance adds 18 vCPUs, 96 GB of RAM and 900 GB of NVMe storage for €999 per month, excluding VAT. Longer commitments cut the price by 15 percent for 12 months or 20 percent for 24 months. Ubuntu 24.04 and CUDA come preinstalled, so either engine runs within minutes of your first login.
The 96 GB of VRAM can hold a 70-billion-parameter model on a single card. That capacity also lets you test SGLang and vLLM side by side on identical hardware. Run each engine against your real prompts, compare throughput and latency, then commit to the winner. Workloads that need several GPUs for tensor parallelism require a larger multi-GPU server instead.
FAQ: SGLang vs vLLM
Is SGLang faster than vLLM?
Sometimes, but not always. In SGLang vs vLLM comparisons, SGLang often leads on workloads with heavy prefix reuse, such as agents, multi-turn chat and structured output. vLLM often matches or beats it on mixed traffic with short, unrelated prompts. Both projects ship performance improvements with nearly every release, so older benchmarks age quickly. Model architecture, quantization, batch size and context length all shift the results. The only reliable answer comes from testing both engines with your own model and prompts on your target GPU.
Can I switch between SGLang and vLLM later?
Yes, and the switch is usually painless. Both engines expose OpenAI-compatible endpoints, so your application code stays the same. In most cases, you only change the base URL and port. SGLang listens on port 30000 by default, while vLLM uses port 8000.
To serve a model with SGLang, run:
sglang serve Qwen/Qwen3-8B –host 0.0.0.0 –port 30000
To serve the same model with vLLM, run:
vllm serve Qwen/Qwen3-8B –host 0.0.0.0 –port 8000
Launch flags and tuning options differ between the engines, so review your configuration when you migrate. Features outside the OpenAI standard, such as the SGLang frontend language, also need rework.
Do both need the same GPU hardware?
Mostly, yes. Both engines run best on modern NVIDIA GPUs with CUDA, and both support AMD GPUs through ROCm. Each also offers CPU and other accelerator backends, though with narrower model support and lower speed. VRAM is the real constraint, not the engine. A model that fits on a GPU under vLLM also fits under SGLang, with similar headroom for the KV cache. Check each project’s documentation for the minimum GPU generation and CUDA version before you deploy.
