Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://pytorch.org/blog/sglang-joins-pytorch/ below:

Efficient LLM Serving Engine – PyTorch

We’re thrilled to announce that the SGLang project has been integrated into the PyTorch ecosystem! This integration ensures that SGLang aligns with PyTorch’s standards and practices, providing developers with a reliable and community-supported framework for fast and flexible serving of LLMs.

To view the PyTorch Ecosystem, see the PyTorch Landscape and learn more about how projects can join the PyTorch Ecosystem.

SGLang is a fast-serving engine for large language models and vision language models. It makes the interaction with models faster and more controllable by co-designing the backend runtime and frontend language.

The core features include:

• Fast Backend Runtime: Provides efficient serving with RadixAttention for prefix caching, zero-overhead CPU scheduler, continuous batching, token attention (paged attention), speculative decoding, tensor parallelism, chunked prefill, structured outputs, and quantization (FP8/INT4/AWQ/GPTQ).
• Flexible Frontend Language: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.
• Extensive Model Support: Supports a wide range of generative models (Llama, Gemma, Mistral, Qwen, DeepSeek, LLaVA, etc.), embedding models (e5-mistral, gte, mcdse) and reward models (Skywork), with easy extensibility for integrating new models.
• Active Community: SGLang is open source and backed by an active community with industry adoption.

SGLang is famous for its fast speed. It can often significantly outperform other state-of-the-art frameworks in terms of serving throughput and latency. You can learn more about the underlying techniques from the past release blog posts: v0.2 blog, v0.3 blog, v0.4 blog.

SGLang has been widely adopted by leading industry companies and frontier research labs. For example, xAI uses SGLang to serve its flagship model, Grok 3, which is currently the best model according to the Chatbot Arena leaderboard. Microsoft Azure uses SGLang to serve DeepSeek R1 on AMD GPUs, which is currently the best open source model.

You can easily launch a Docker container to serve a DeepSeek model with the following command:

# Pull the latest image
docker pull lmsysorg/sglang:latest

# Launch a server
docker run --gpus all --shm-size 32g -p 30000:30000 -v ~/.cache/huggingface:/root/.cache/huggingface --ipc=host --network=host --privileged lmsysorg/sglang:latest \
    python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V3 --tp 8 --trust-remote-code --port 30000

Then you can query the server with the OpenAI-compatible API

import openai
client = openai.Client(base_url=f"http://127.0.0.1:30000/v1", api_key="None")

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V3",
    messages=[
        {"role": "user", "content": "List 3 countries and their capitals."},
    ],
    temperature=0,
    max_tokens=64,
)

The server launch command above works for 8xH200. You can find detailed instructions for other hardware (MI300X, H100, A100, H20, L40S) at https://docs.sglang.ai/references/deepseek.html.

SGLang integrates DeepSeek-specific optimizations, such as MLA throughput optimizations, MLA-optimized kernels, data-parallel attention, multi-token prediction, and DeepGemm, making it the top choice for serving DeepSeek models by dozens of companies, including AMD, NVIDIA, and many cloud providers. The team is actively working on integrating more optimizations following the 2025 H1 roadmap below.

Similarly, you can launch the server for a Llama 3.1 text model with:

python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct

Or a Llama 3.2 multimodal model with:

python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-11B-Vision-Instruct  --chat-template=llama_3_vision

This year, the SGLang team will continue to push the boundaries of system efficiency. You can find the roadmap of 2025H1 here. The focus is

• Throughput-oriented large-scale deployment similar to the DeepSeek inference system
• Long context optimizations
• Low latency speculative decoding
• Reinforcement learning training framework integration
• Kernel optimizations

SGLang has been deployed to large-scale production, generating trillions of tokens every day. It has an active community with over three hundred contributors on GitHub. It is supported by the following institutions: AMD, Atlas Cloud, Baseten, Cursor, DataCrunch, Etched, Hyperbolic, iFlytek, Jam & Tea Studios, LinkedIn, LMSYS, Meituan, Nebius, Novita AI, NVIDIA, RunPod, Stanford, UC Berkeley, UCLA, xAI, and 01.AI.

We’re excited to welcome SGLang to the PyTorch ecosystem. SGLang accelerates the serving of large language and vision language models. It’s widely adopted by industry, powering the large-scale online serving of frontier models like Grok and DeepSeek.

We invite you to explore the SGLang GitHub repo, join the community on Slack, and reach out to contact@sglang.ai for inquiries or collaboration opportunities. Together, we can make powerful AI models accessible to everyone.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4