SGLang

SGLang is a high-performance serving engine built for teams that need throughput on par with the fastest engines in the market plus first-class support for structured output and reusable prompt prefixes. Maintained by the SGLang Project and hosted under the non-profit open-source organization LMSYS, it was first released in 2024 and has quickly become a go-to choice for agent workloads, pipeline serving, and high-volume production deployments that demand strict output formatting.

Written in Python and licensed under Apache 2.0, SGLang is categorized as a serving engine — not a training framework or a general-purpose inference library. It competes directly with vLLM on raw throughput while adding capabilities that matter when you are running structured generation at scale: RadixAttention for prefix caching, built-in constrained decoding, and an OpenAI-compatible API. With nearly 30,000 GitHub stars, 460+ contributors, and over 486 million PyPI monthly downloads, SGLang has built substantial traction in a short time. The project claims it powers deployments across more than 400,000 GPUs globally, generating trillions of tokens daily.

The team behind SGLang, including researchers from Stanford and UC Berkeley, designed the engine around the observation that many production LLM calls reuse shared prompt prefixes (e.g., system instructions, context documents). Instead of recomputing the KV cache for every request, SGLang caches and reuses those prefixes using a radix tree structure. This is not a trivial optimization — it directly reduces latency and compute cost for the kinds of workloads teams actually run: agents, RAG chains, multi-turn chats, and structured extraction pipelines.

You serve a model with SGLang by launching a Python-based server that exposes an OpenAI-compatible HTTP API. The core primitives are simple: load a model, start the server, and send requests using any OpenAI client library. No custom client code is required. For most existing toolchains (LangChain, LlamaIndex, custom scripts), swapping in SGLang means changing the base URL.

The runtime model centers on a single process that manages GPU memory, continuous batching, and request scheduling. You load a model by specifying its Hugging Face identifier or a local path. SGLang handles model sharding across multiple GPUs automatically. The server supports streaming output, structured output constraints (via JSON schema or grammar), and the full set of OpenAI chat completion parameters (temperature, top_p, etc.).

To get a model serving in minutes:

1pip install "sglang[all]"
2python -m sglang.launch_server --model meta-llama/Meta-Llama-3-8B-Instruct --port 30000

Then send a request with curl or an OpenAI client:

1curl http://localhost:30000/v1/chat/completions -d '{
2  "model": "default",
3  "messages": [{"role": "user", "content": "Hello"}]
4}'

SGLang also provides a Python API for programmatic use, but the primary deployment path is the server. It supports Docker deployments and works with orchestration tools like Kubernetes for production clusters.

SGLang delivers throughput competitive with the fastest serving engines — often matching or exceeding vLLM on standard benchmarks, and pulling ahead on agent workloads where prefix reuse matters. The key performance levers are continuous batching, RadixAttention for KV cache reuse, and multi-GPU parallelism.

Hardware support: Confirmed support includes NVIDIA GPUs and AMD GPUs. The documentation also lists Intel Xeon, Google TPU, and Ascend NPU accelerators as supported platforms. However, in practice, the engine is GPU-focused. CPU inference and Apple Silicon support are limited or experimental. If you need to run on a MacBook or a CPU-only server, SGLang is not the right choice — look at llama.cpp or Ollama instead.

Memory and quantization: SGLang supports quantization (including GPTQ, AWQ, and FP8) to reduce memory footprint. It uses paged attention for efficient KV cache management, similar to vLLM. On multi-GPU setups, it supports tensor parallelism and pipeline parallelism to scale across nodes.

Latency vs throughput: Like vLLM, SGLang is optimized for throughput — making it ideal for batch serving, agent loops, and workloads that can tolerate moderate per-request latency in exchange for high total output. For extremely low-latency single-request serving, the overhead of continuous batching may be less beneficial, but SGLang still performs well at high concurrency.

Where it falls short: If you need to run on CPU or Apple Silicon, or if your hardware is older than NVIDIA Ampere (compute capability 7.0+), SGLang may not work or will be slow. The community is smaller than vLLM’s, so advanced troubleshooting can take longer.

RadixAttention is SGLang’s signature optimization. It stores KV cache entries in a radix tree keyed by token sequences. When multiple requests share a common prefix (e.g., the same system prompt or retrieved context), that prefix’s KV cache is reused instead of recomputed. This directly reduces time-to-first-token for subsequent requests with overlapping prefixes.

In practice, RadixAttention makes a measurable difference for workloads like:

• Multi-turn chat where conversation history repeats
• Agent loops that call the same function with different parameters
• RAG pipelines where the same document is prepended to each query
• Few-shot prompting with fixed examples

SGLang includes a built-in constrained decoding engine that forces output to match a JSON schema, regular expression, or context-free grammar. This is implemented using compressed finite state machines, which reduce the overhead of structured generation compared to naive masking approaches.

For teams that need guaranteed valid JSON from a model — for data extraction, API integration, or function calling — this feature eliminates the need for post-processing or retry logic. You specify the schema, and SGLang ensures every token generated is compliant.

The server exposes an API that mirrors the OpenAI chat completions endpoint. Most OpenAI client libraries connect without changes. This means you can drop SGLang into existing applications that already use the OpenAI SDK without modifying a single line of client code. Streaming, function calling, and tool calls are all supported.

Confirmed capabilities include structured output, streaming, continuous batching, multi-GPU scaling, quantization, and support for both NVIDIA and AMD GPUs. SGLang also supports multimodal models (image + text) and diffusion model serving in recent releases.

This is where SGLang shines. Agent workloads often make dozens of sequential or parallel calls to the same model with slightly different inputs but identical system prompts and tool definitions. RadixAttention directly reduces the cost of those repeated prefixes. Teams running LangGraph, CrewAI, or custom agent frameworks often see 2–3x throughput improvements over engines without prefix caching.

For production pipelines that need to extract JSON from documents (invoices, emails, logs), SGLang’s constrained decoding ensures the output is valid without additional parsing or validation. This is common in AI-driven data processing, ETL pipelines, and enterprise automation.

If you are serving a chat model or an API endpoint to hundreds or thousands of concurrent users, SGLang’s throughput matches the fastest alternatives. Its continuous batching and KV cache management keep GPU utilization high. Major deployments claim to serve trillions of tokens per day across hundreds of thousands of GPUs.

SGLang is not ideal for local development on laptops without a discrete GPU, edge deployments on ARM devices, or scenarios requiring strict CPU-only operation. It also has a smaller ecosystem of third-party tooling compared to vLLM, so if you need deep integration with specific observability or infrastructure tools, check compatibility first.

The fastest path to a running model:

1. Install with pip: pip install "sglang[all]" (requires Python 3.9+ and a compatible GPU with CUDA 11.8+ or ROCm for AMD).
2. Start the server: python -m sglang.launch_server --model meta-llama/Meta-Llama-3-8B-Instruct --port 30000
3. Send a request from any OpenAI client.

You can also use Docker: docker run --gpus all -p 30000:30000 lmsysorg/sglang:latest python -m sglang.launch_server --model meta-llama/Meta-Llama-3-8B-Instruct.

For local testing without a GPU, you can run with CPU-only (not recommended for production). Quantized models (GPTQ, AWQ) reduce VRAM requirements.

Find the documentation at docs.sglang.ai, the GitHub repository at github.com/sgl-project/sglang, and community support on Slack.

SGLang vs vLLM: These two are the most direct competitors. Both offer continuous batching, paged attention, OpenAI-compatible APIs, and high throughput. vLLM has a larger community, more third-party integrations, and wider hardware support (including some CPU/Apple Silicon options). SGLang wins on structured output and prefix caching. If you need guaranteed JSON output or run agent workloads with heavy prefix reuse, choose SGLang. If you need broader ecosystem support or CPU inference, choose vLLM.

SGLang vs Ollama: Ollama is designed for local development and ease of use, not production throughput. It wraps llama.cpp and offers a simpler CLI and API. SGLang is for teams that need to serve at scale. If you are prototyping on a laptop, use Ollama. If you are deploying to a cluster, use SGLang or vLLM.

SGLang vs TensorRT-LLM: TensorRT-LLM is NVIDIA’s optimised engine, offering the highest possible performance on NVIDIA hardware but with a steeper learning curve and tighter coupling to NVIDIA. SGLang is more hardware-agnostic (supports AMD, Intel, TPU) and easier to set up. If you are all-in on NVIDIA and need every last drop of performance, consider TensorRT-LLM. For broader hardware support and easier operation, SGLang is a stronger choice.

Bottom line: SGLang is a production-ready serving engine that matches the top performers on throughput and adds meaningful capabilities for structured output and agent workloads. The trade-offs are limited GPU support and a smaller community. If those constraints fit your stack, SGLang is a serious alternative to vLLM for high-volume, structured generation tasks.