Source
Mini-SGLang is a compact implementation of SGLang, designed to demystify the complexities of modern LLM serving systems. With a compact codebase of ~5,000 lines of Python, it serves as both a capable inference engine and a transparent reference for researchers and developers.
Key Features
- High Performance: Achieves state-of-the-art throughput and latency with advanced optimizations.
- Lightweight & Readable: A clean, modular, and fully type-annotated codebase that is easy to understand and modify.
- Advanced Optimizations:
- Radix Cache: Reuses KV cache for shared prefixes across requests.
- Chunked Prefill: Reduces peak memory usage for long-context serving.
- Overlap Scheduling: Hides CPU scheduling overhead with GPU computation.
- Tensor Parallelism: Scales inference across multiple GPUs.
- Optimized Kernels: Integrates FlashAttention and FlashInfer for maximum efficiency.
Tags:
language
ai
machine learning
Last modified 15 January 2026