vLLM vs SGLang for self-hosted inference

Summary

• vLLM uses PagedAttention for efficient key-value cache management, enabling high GPU memory utilization across large batches of concurrent requests.
• SGLang uses RadixAttention for prefix cache reuse, making it effective for workloads where many requests share a common system prompt or context prefix.
• Both support OpenAI-compatible APIs, speculative decoding, and multiple hardware backends; the differences lie in which optimization each prioritizes.
• vLLM supports a broader range of quantization formats and model architectures out of the box; SGLang emphasizes prefix-heavy workloads and has a proven integration path for reinforcement learning training pipelines.
• On Databricks Model Serving, GPU-backed endpoints provide the deployment infrastructure; the choice between vLLM and SGLang follows from the request distribution and workload characteristics.

vLLM vs SGLang for self-hosted inference

Both vLLM and SGLang solve the same core problem: serve large language models at high throughput with low latency on GPU hardware. They are open-source, production-grade, and OpenAI-compatible. The differences are in which problem each optimizes for. vLLM optimizes GPU memory use through PagedAttention. SGLang optimizes key-value cache reuse through RadixAttention for workloads where many requests share a prefix.

What are vLLM and SGLang?

vLLM is a high-throughput LLM inference engine whose core innovation, PagedAttention, manages KV-cache memory with a paged approach borrowed from operating-system virtual memory management. This allows efficient handling of variable-length sequences in large batches without memory fragmentation. vLLM is maintained as open-source software with dedicated stable release branches.
SGLang is a high-performance serving framework for LLMs and multimodal models, designed for low-latency, high-throughput inference. Its core innovation, RadixAttention, enables efficient reuse of cached key-value pairs across requests that share a common prefix. SGLang is developed by LMSYS, a nonprofit research organization, and serves as a production inference backend at scale.

vLLM vs SGLang: how they compare

Capability	vLLM	SGLang
Core KV-cache mechanism	PagedAttention: paged KV-cache allocation	RadixAttention: prefix-aware cache reuse
Best for	Diverse request distributions, broad model coverage	Repeated-prefix workloads, shared system prompts, RL training
Model coverage	A broad range of architectures on Hugging Face (LLMs, MoE, multimodal, embedding, reward)	Llama, Qwen, DeepSeek, Kimi, GLM, embedding and diffusion models; day-0 new-model support
Hardware backends	NVIDIA/AMD GPUs, Intel Gaudi, Google TPUs, Apple Silicon, x86/ARM/PowerPC CPUs, Huawei Ascend, IBM Spyre	NVIDIA (GB200/B300/H100), AMD (MI355/MI300), Intel Xeon CPUs, Google TPUs, Ascend NPUs
Quantization	FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, GPTQ/AWQ, GGUF, compressed-tensors, TorchAO	Standard quantization formats
Speculative decoding	n-gram, suffix, EAGLE, DFlash variants	DFlash, Spec V2/MTP variants
Structured outputs	xgrammar or guidance	Compressed finite state machines
Distributed inference	Tensor, pipeline, data, expert, context parallelism	Multi-GPU parallelism, prefill-decode disaggregation
RL/training integration	Standard	Proven rollout backend for RL training frameworks (Miles, verl)
OpenAI API compatibility	Yes, plus Anthropic Messages API and gRPC	Yes

Sources: vLLM - docs.vllm.ai; SGLang - docs.sglang.io and GitHub repository

How to choose between vLLM and SGLang

The practical decision turns on your request distribution.
For applications with diverse requests and little prefix overlap - users asking varied questions with few repeated prompts - vLLM's PagedAttention handles KV-cache memory efficiently across varying sequence lengths. Its broad quantization support and wide model architecture coverage make it a flexible choice when model format or hardware compatibility is uncertain.
For applications where many requests share a large common prefix - a shared system prompt of hundreds of tokens, repeated context blocks in agentic pipelines, or retrieval-augmented generation where the same retrieved documents appear across requests - SGLang's RadixAttention reuses that cached computation across requests rather than recomputing it each time. The cache hit rate from RadixAttention is highest when prefix sharing is high and consistent.
For reinforcement learning training pipelines, SGLang has established integration paths with RL frameworks like Miles and verl as a rollout inference backend. If your team trains models and needs the inference engine to participate in an RL loop, SGLang has a more developed path there.

How Databricks supports LLM inference

Databricks Model Serving provides GPU-backed endpoints for LLM deployment with auto-scaling, Unity Catalog integration for access control, and support for both provisioned throughput and pay-per-token pricing on curated foundation models.
The Foundation Model APIs provide hosted access to curated models with performance guarantees for teams that don't need to manage inference infrastructure directly.
For specialized workloads with unique inference requirements, you can bring custom inference engines into Databricks infrastructure for configuration control, scaling, and governance support.

FAQs

Which framework has broader hardware support?

vLLM documents support for a wider range of hardware: NVIDIA/AMD GPUs, Intel Gaudi, Google TPUs, Apple Silicon, multiple CPU architectures, and several specialized accelerators. SGLang focuses primarily on NVIDIA and AMD GPUs, Intel Xeon CPUs, Google TPUs, and Ascend NPUs.

How does multi-LoRA serving compare between vLLM and SGLang?

vLLM explicitly documents multi-LoRA support across dense and MoE model layers for serving multiple fine-tuned adapters concurrently. Both frameworks support LoRA fine-tuned models, but vLLM's documentation covers the concurrent multi-adapter serving path in more detail.

When does speculative decoding help?

Speculative decoding reduces latency by having a smaller draft model propose tokens that the larger model then verifies in a single pass. Both vLLM and SGLang support it. The benefit depends on draft-model quality and request length: longer outputs from predictable patterns benefit the most.

Can the same model be served by either framework?

In most cases, yes. Both frameworks support the major open-source model families (Llama, Qwen, DeepSeek, Mistral, and others). vLLM's architecture support is broader, so for less common model formats or older architectures, vLLM may have ready support where SGLang requires a custom implementation.