Optimize Performance of AI inference and training through revolutionary software-defined AI-native data pipeline
Optimize Performance of AI inference and training through revolutionary software-defined AI-native data pipeline
At TensorMem, we envision a future where AI Infrastructure operates at peak performance. Our goal is to eliminate the critical bottlenecks that hinder efficiency, and maximise ROI in AI deployments.
AI infrastructure pours millions into GPUs, yet performance collapses as memory and storage bottlenecks leave world-class silicon waiting on data instead of delivering intelligence.
We enable AI-native Agentic platforms and Neo-scalers to maximise ROI and lower total cost of ownership by fully utilising their exiting AI investments, bridging performance gaps in GPU and AI infrastructure.
IIT Bombay graduate, serial entrepreneur with two successful startup exits and 67 US patents. Former Chief Architect and CTO at Veritas and Arctera. Deep expertise in Distributed Software-defined Storage, Infrastructure, Cyber Resilience, and cloud-scale enterprise solutions.
IIT Kharagpur graduate, 15 US patents, and founding engineer of MapR Technologies - a pioneering distributed file system platform for large-scale analytics. Deep expertise in high-performance data infrastructure and hyper-scalable enterprise solutions.