![]() Furthermore, it integrates low-overhead load-balancer that offers perfect linear scaling on dozens of replicas. This results in improved responsiveness, efficiency, and lower variance, providing lower latency and higher throughput streaming generation to all clients compared to other serving systems.Īccording to Samyam Rajbhandari on LinkedIn:įastGen effectively synthesizes novel batch scheduling techniques with efficient KV cahce management, communication optimized tensor-parallelsim and ultra fast CUDA kernels. It allows DeepSpeed-FastGen to run at a consistent forward size by taking partial tokens from prompts and composing this with generation. SplitFuse enables it to offer up to 2.3 times higher effective throughput compared to systems like vLLM. ![]() The Dynamic SplitFuse technique is a new token composition strategy for prompt processing and token generation. The system currently supports several model architectures. DeepSpeed-FastGen is based on the Dynamic SplitFuse technique. DeepSpeed-FastGen is the synergistic composition of DeepSpeed-MII and DeepSpeed-Inference.
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