This article introduces Tensor R-Fork, a method for quickly loading model weights in SGLang instances using GPU-Direct RDMA. It significantly reduces loading times and storage requirements while allowing uninterrupted inference services. The article details the implementation using two backends: NCCL and TransferEngine.

SGLang has integrated Hugging Face transformers as a backend, enhancing inference performance for models while maintaining the flexibility of the transformers library. This integration allows for high-throughput, low-latency tasks and supports models not natively compatible with SGLang, streamlining deployment and usage. Key features include automatic fallback to transformers and optimized performance through mechanisms like RadixAttention.