Large Language Models (LLMs) are gaining traction, but effective methods for their development and operation are lacking. LMSYS ORG introduces SGLang, a language enhancing LLM interactions, and RadixAttention, a method for automatic KV cache reuse, optimizing LLM performance. SGLang enables simpler and faster LLM programming, outperforming current systems by a factor of up to five in throughput.
Introducing SGLang: Fast and Expressive LLM Inference with RadixAttention
Backend: Automatic KV Cache Reuse with RadixAttention
SGLang introduces RadixAttention, a new automatic KV cache reuse method, optimizing the backend runtime system. This improves cache hit rates and enables efficient search, insertion, and eviction of prefixes, enhancing the speed and controllability of Large Language Models (LLMs).
Frontend: Easy LLM Programming with SGLang
SGLang, an embedded domain-specific language in Python, simplifies complex LLM programming, including prompting, control flow, and external interaction. It offers a user-friendly approach to running functions through various models.
Performance and Benchmarks
When tested on typical LLM workloads, SGLang outperformed current systems by up to five times in throughput. It also demonstrated impressive latency performance, particularly in scenarios involving prefix cache hits.
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