| [**Join Bi-Weekly Development Meeting**](https://meeting.sglang.ai/)
...
...
@@ -44,7 +44,7 @@ SGLang is a fast serving framework for large language models and vision language
It makes your 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, quantization (FP8/INT4/AWQ/GPTQ), and multi-lora batching.
-**Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, zero-overhead CPU scheduler, prefill-decode disaggregation, speculative decoding, continuous batching, paged attention, tensor parallelism, pipeline parallelism, expert parallelism, structured outputs, chunked prefill, quantization (FP8/INT4/AWQ/GPTQ), and multi-lora batching.
-**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.
...
...
@@ -63,7 +63,7 @@ Learn more in the release blogs: [v0.2 blog](https://lmsys.org/blog/2024-07-25-s
SGLang has been deployed at large scale, serving trillions of tokens in production every day. It is trusted and adopted by a broad range of leading enterprises and institutions, including xAI, NVIDIA, AMD, Google Cloud, Oracle Cloud, LinkedIn, Cursor, Voltage Park, Atlas Cloud, DataCrunch, Baseten, Nebius, Novita, InnoMatrix, RunPod, Stanford, UC Berkeley, UCLA, ETCHED, Jam & Tea Studios, Hyperbolic, as well as major technology organizations across North America and Asia. As an open-source LLM inference engine, SGLang has become the de facto standard in the industry, with production deployments running on over 100,000 GPUs worldwide.
SGLang has been deployed at large scale, generating trillions of tokens in production every day. It is trusted and adopted by a broad range of leading enterprises and institutions, including xAI, NVIDIA, AMD, Google Cloud, Oracle Cloud, LinkedIn, Cursor, Voltage Park, Atlas Cloud, DataCrunch, Baseten, Nebius, Novita, InnoMatrix, RunPod, Stanford, UC Berkeley, UCLA, ETCHED, Jam & Tea Studios, Hyperbolic, as well as major technology organizations across North America and Asia. As an open-source LLM inference engine, SGLang has become the de facto standard in the industry, with production deployments running on over 100,000 GPUs worldwide.
@@ -5,7 +5,7 @@ SGLang is a fast serving framework for large language models and vision language
It makes your 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, quantization (FP8/INT4/AWQ/GPTQ), and multi-lora batching.
- **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, zero-overhead CPU scheduler, prefill-decode disaggregation, speculative decoding, continuous batching, paged attention, tensor parallelism, pipeline parallelism, expert parallelism, structured outputs, chunked prefill, quantization (FP8/INT4/AWQ/GPTQ), and multi-lora batching.
- **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.
