Samyam Rajbhandari is an expert on AI Systems. He currently leads the inference optimization efforts at Snowflake responsible for development of technologies like SwiftKV for reducing cost and latency of inference. Prior to his currenr role, he co-led the design and development of Snowflake Arctic, an innovative foundation model with a unique MoE architecture, capabile of achieving state-of-the-art enterprise intelligence with best-in-class cost efficiency at the time of release.
Prior to Snowflake, Samyam was a co-founder and the system architect for DeepSpeed at Microsoft, where he worked on developing high-performance infrastructures for accelerating large-scale deep learning training and inference on parallel and distributed systems. He designed systems such as ZeRO and 3D parallelism that have been adoptedby many DL frameworks, has become the staple engine for training large language models including models like meta LLama and many other LLMs.
On the inference front, he led the effort to optimize the inference system for Dalle.2 and Dall.E 3 models from OpenAI to reduce latency, cost and improve capacity. He has also designed fast systems and led optimization efforts that have been released as part of DeepSpeed-Inference/MII and used in products such as Bing, Ads, AzureML within Microsoft.
Samyam received his PhD in High Performance Computing from The Ohio State University.
