SGLang: Accelerating Structured LLM Inference with RadixAttention

Large Language Models (LLMs) have taken over application development, but serving them efficiently remains a major engineering challenge. Standard autoregressive generation is slow and computationally expensive. In complex agentic workflows, multi-turn dialogues, and structured outputs (like JSON or regular expressions), traditional runtimes waste massive amounts of compute re-processing the same prompt prefixes.

Enter SGLang (Structured Generation Language)—a state-of-the-art LLM serving and programming framework designed to drastically accelerate LLM workflows by co-designing the front-end language and the back-end runtime.

In this post, we’ll explore what SGLang is, its benefits, what happens under the hood, how it manages inference, whether it supports training, and how it stacks up against vLLM.

What is SGLang?

SGLang is an open-source, high-performance engine designed for serving and programming LLMs. Developed by researchers at UC Berkeley, Stanford, and other top institutions, SGLang co-designs two major layers:

1. The Front-End (DSL): A Python-embedded domain-specific language (DSL) that allows developers to write complex LLM programs. It enables robust control over multi-turn dialogues, parallel generation, multimodal inputs, and schema-constrained decoding (e.g., forcing the LLM to output valid JSON).
2. The Back-End (Runtime): The SGLang Runtime (SRT) is a high-throughput serving engine. It includes optimizations like RadixAttention for KV cache sharing, fast constrained decoding, chunked prefill, and native tensor/pipeline parallelism.

# A simple example of SGLang DSL in action
from sglang import gentle, gen, select

@gentle
def math_qa(s, question):
    s += "Question: " + question + "\n"
    s += "Reasoning:\n"
    s += gen("reasoning", stop="\n")
    s += "\nFinal Answer: "
    s += select("answer", choices=["A", "B", "C", "D"])

The Benefits of SGLang

SGLang offers distinct advantages over standard inference pipelines:

• Exceptional Throughput: By utilizing advanced cache reuse and optimized execution kernels, SGLang can achieve up to 2x to 5x higher throughput than standard serving engines, especially in complex structured generation workloads.
• Zero-Overhead Prefix Sharing: For multi-turn chats, few-shot prompting, and agentic workflows, the prompt prefixes are automatically cached and shared across requests without developer intervention.
• Fast Constrained/Structured Generation: SGLang features a co-designed compiler for JSON schema and regular expression constraints, minimizing token-level latency during structured decoding.
• State-of-the-Art Multimodal Support: SRT natively integrates multimodal processing (images, video, text) in high-throughput production environments.

Under the Hood: How SGLang Works

At the heart of SGLang’s performance lies RadixAttention—a revolutionary approach to Key-Value (KV) cache management.

In traditional systems like vLLM (using PagedAttention), prompt caches are stored linearly. While vLLM can cache static system prefixes via a simple hash table (Automatic Prefix Caching), it struggles with complex dynamic prefixes (e.g., intermediate steps in an agent loop or multi-turn trees).

SGLang models the entire KV cache in GPU memory as a Radix Tree:

               [Root: "System: You are an AI assistant..."]
                               /          \
                              /            \
     [Turn 1: "Describe quantum physics"]  [Turn 1: "Explain deep learning"]
                             |                              |
            [Response: "Sure, it is..."]        [Response: "Deep learning is..."]

The RadixAttention Lifecycle

1. Prefix Matching: When a new request arrives, SGLang traverses the Radix Tree from the root to locate the longest matching prefix already resident in the KV cache.
2. Cache Reuse: The matched prefix tokens bypass the prefill phase entirely. SGLang reuses the cached KV tensors directly from memory, eliminating massive prompt-processing latency.
3. Dynamic Branching: SGLang appends the new prompt tokens as a child branch from the matching node and begins generating new tokens.
4. Least Recently Used (LRU) Eviction: When GPU memory is constrained, the runtime evicts nodes that haven’t been accessed recently. Crucially, because it is structured as a tree, it can evict leaves while keeping core branches (like system prompts) intact.

What Happens During Inference?

When you call the SGLang server for inference:

1. Request Parsing: The server accepts standard OpenAI-compatible requests or SGLang DSL queries.
2. KV Cache Lookup: RadixAttention identifies any cached prompt segments.
3. Chunked Prefill: For the remaining uncached prompt tokens, SGLang uses a chunked prefill strategy to process prompt tokens in small blocks, preventing large prefill tasks from blocking active generation tasks.
4. Constrained Decoding (If Applicable): If you requested a specific schema (e.g., JSON), SGLang builds a finite-state machine (FSM) from your regex or schema. During generation, it masks out invalid tokens before computing softmax, guaranteeing valid outputs with virtually zero latency penalty.
5. Streaming: Tokens are streamed back to the client as they are decoded in the radix tree.

Can You Finetune Models with SGLang?

[!IMPORTANT] SGLang is strictly an Inference and Serving Engine. SGLang does NOT support training or finetuning models (such as full finetuning, LoRA, QLoRA, or RLHF training).

Once you have trained your model and saved the weights (or exported LoRA adapters), you can load them directly into SGLang. SGLang supports Dynamic LoRA serving, allowing you to serve multiple different finetuned task adapters on top of a single base model concurrently with negligible overhead.

What do researchers use for faster training and RLHF?

For accelerating training, supervised finetuning (SFT), and Reinforcement Learning from Human/AI Feedback (RLHF), researchers in academia and industry use a tiered stack of specialized frameworks:

1. Small-to-Medium Scale Finetuning (1 to 8 GPUs)

When researchers want to adapt pre-trained models to specific domains using SFT or Parameter-Efficient methods (LoRA/QLoRA), they turn to highly optimized libraries:

• Unsloth: Highly recommended for single or multi-GPU setups. It provides 2x to 5x faster training and uses up to 80% less memory by using hand-optimized Triton kernels that replace PyTorch’s default autograd backward pass for attention and loss layers.
• Axolotl: The go-to tool in the open-source community for highly reproducible runs. It uses clean YAML configuration files to orchestrate advanced optimizations like sequence packing (eliminating waste padding), FlashAttention-2, DeepSpeed, and PyTorch FSDP.
• LLaMA-Factory: An incredibly popular, all-in-one suite offering a massive catalog of supported models, algorithms, and a web-based training dashboard (LLaMA-Board) for zero-code training runs.

2. Large-Scale Distributed & Foundation Training (Hundreds of GPUs)

For training foundation models from scratch or full-parameter tuning at massive scale, researchers use frameworks designed for 3D Parallelism (Data, Tensor, and Pipeline parallelism):

• Megatron-LM (NVIDIA): The gold standard for multi-node, massive model training. It features custom CUDA kernels and native GPU-to-GPU model sharding.
• DeepSpeed (Microsoft): Extremely popular in both industry and academia. Its ZeRO (Zero Redundancy Optimizer) technology shards optimizer states, gradients, and model parameters across standard data-parallel clusters to train massive models without out-of-memory errors.
• PyTorch FSDP (Fully Sharded Data Parallel): Meta’s native PyTorch alternative to DeepSpeed ZeRO-3. Highly integrated with PyTorch compiled kernels, FSDP was a primary system pillar used in training Llama 3.

3. Reinforcement Learning & Preference Alignment (RLHF / RLAIF)

Training with RL (PPO, DPO, KTO) is complex because it involves coordinating multiple models at once: an Actor (generating text), a Reference model, a Critic, and a Reward model.

• OpenRLHF: A state-of-the-art framework for PPO, DPO, and RLAIF. It uses Ray and DeepSpeed to coordinate training across GPUs. Crucially, it speeds up the generation bottleneck in PPO by leveraging vLLM or SGLang as high-speed generation roll-out engines during the training loops.
• Hugging Face TRL (Transformer Reinforcement Learning): Natively supports DPO (Direct Preference Optimization), ORPO, and KTO. Since DPO bypasses active token generation during training, TRL makes preference alignment as simple and fast as standard Supervised Finetuning.

4. Scaling & Training Diffusion Models (DiTs)

As the field of image and video generation has moved away from traditional U-Nets to Diffusion Transformers (DiTs) (like Stable Diffusion 3, Flux.1, and OpenAI’s Sora), the distinction between language model training and diffusion training has blurred. Because DiTs are composed of standard transformer blocks, they use the exact same systems-level training stack:

• DeepSpeed & Megatron-LM: Researchers use these to shard massive video and image diffusion transformers across multi-node systems. Without ZeRO memory sharding and pipeline parallelism, training modern DiTs with billions of parameters would be impossible.
• Kohya_ss: The go-to open-source GUI and CLI suite for training Diffusion LoRAs, LyCORIS, and custom text-to-image/text-to-video finetunes on consumer-grade hardware.

SGLang & vLLM for Native Diffusion Inference

While SGLang and vLLM were historically built solely for autoregressive language model serving, they have both recently expanded to natively support high-performance Diffusion Transformer (DiT) serving for image and video generation (e.g., Flux.1, HunyuanVideo, and Wan).

This native support represents a major shift: researchers no longer have to build custom inference pipelines with raw PyTorch or Hugging Face Diffusers for production scale.

1. SGLang Diffusion

SGLang now includes a dedicated high-performance diffusion serving subsystem designed for ultra-low latency image and video generation.

• Cache-DiT Integration: SGLang natively implements Cache-DiT, an advanced training-free acceleration framework. Cache-DiT speeds up inference by caching intermediate computation results across denoising steps, allowing the engine to skip redundant blocks via DBCache (Dual Block Cache) based on residual differences, or use TaylorSeer to predict future hidden states.
• Custom Kernels & Parallelism: By pairing Cache-DiT with SGLang’s custom GPU kernels (sgl-kernel) and advanced sequence/tensor parallelism, it achieves up to 20% to 169% higher throughput than baseline serving scripts.
• Production Serving: SGLang exposes this via an OpenAI-compatible API, a unified command-line interface, and Python APIs, letting teams serve image/video generation with the same reliable cluster infrastructure used for LLMs.

2. vLLM-Omni

In parallel, the vLLM project has introduced vLLM-Omni, an architectural extension that facilitates “omnimodal” inference (unifying text, image, audio, and video) under a single serving umbrella.

• Decoupled Multi-Modal Pipelines: Unlike running separate servers, vLLM-Omni coordinates the main LLM (the brain) and a non-autoregressive generator (e.g., a diffusion model for image/video) inside a single, distributed runtime.
• Media Acceleration Features: It integrates classifier-free guidance parallelism (CFG-Parallel), Cache-DiT, and TeaCache to dynamically skip redundant denoising steps in media generation.

SGLang vs. vLLM

While vLLM is the most widely adopted LLM serving engine, SGLang is rapidly gaining ground, particularly for production agent systems. Here is a head-to-head comparison:

Summary Recommendation

• Choose SGLang if your workflow involves heavily structured outputs (JSON schemas), multi-turn agent conversations, tree-of-thought prompting, or few-shot examples where prompt segments are frequently repeated.
• Choose vLLM if you need the absolute widest ecosystem compatibility, simple deployment setups, or standard single-turn text-generation serving.

Conclusion

SGLang represents a major evolutionary step in LLM systems engineering. By integrating KV cache management with a robust programming model, it provides the speed and control required for building complex AI agents and high-performance production pipelines.