NanoSLG: Multi-GPU LLM Server Achieves 5x Speedup

NanoSLG presents a streamlined approach to multi-GPU LLM inference, focusing on performance and educational value. Its key innovation lies in the dual KV cache backend, which dynamically selects between FlashInfer (for high-end GPUs) and contiguous SDPA (for older GPUs or fallback scenarios). This adaptive caching mechanism, combined with radix prefix caching for shared prompt prefixes, significantly reduces memory pressure and improves throughput. The server's support for pipeline, tensor, and hybrid parallelism modes further enhances scalability, allowing users to optimize performance based on their hardware configuration. Benchmarking results on NVIDIA L4 GPUs demonstrate substantial improvements in tokens/second and time-to-first-token compared to previous versions. NanoSLG's modular design and OpenAI-compatible API facilitate integration with existing AI applications. The project's emphasis on educational value makes it a valuable resource for researchers and developers seeking to understand and optimize LLM inference techniques.

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