Manu Bhadoria
Nvidia, Engineering Manager (GeForce Now)
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Manu is an engineering manager for the Cloud and Infrastructure Services team at NVIDIA, specifically supporting GeForce NOW, a cloud-based game streaming platform, and NVCF, a GPU cloud computing platform. Based in the Bay Area, Manu enjoys reading, hiking, motorcycle rides, running, and crossfitting when not innovating and engineering.
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Area of Expertise
Blazing-Fast Container Deployments: Image Caching with Block Storage
In the fast-paced world of Kubernetes, deploying containerized workloads—especially AI/ML models—demands speed and efficiency. Traditional container image pulls from remote registries often bottleneck deployment times, particularly for large, complex images. This session introduces an innovative solution: caching base container images using remote block storage to significantly enhance deployment times and boost cluster efficiency. We’ll delve into how block storage serves as a backing store to cache container images across all nodes in a Kubernetes cluster. Attendees will explore the architecture, implementation steps, and real-world performance gains from our production deployment for NVIDIA Cloud Functions, showcasing a remarkable 52%+ reduction in deployment times. Whether you’re managing AI-driven microservices or latency-sensitive applications, this session equips you with practical strategies to accelerate your Kubernetes deployments using cutting-edge storage solutions.
Blazing-Fast Container Deployments: Image Caching with Block Storage
In the fast-paced world of Kubernetes, deploying containerized workloads—especially AI/ML models—demands speed and efficiency. Traditional container image pulls from remote registries often bottleneck deployment times, particularly for large, complex images. This session introduces an innovative solution: caching base container images using remote block storage to significantly enhance deployment times and boost cluster efficiency. We’ll delve into how block storage serves as a backing store to cache container images across all nodes in a Kubernetes cluster. Attendees will explore the architecture, implementation steps, and real-world performance gains from our production deployment for NVIDIA Cloud Functions, showcasing a remarkable 52%+ reduction in deployment times. Whether you’re managing AI-driven microservices or latency-sensitive applications, this session equips you with practical strategies to accelerate your Kubernetes deployments using cutting-edge storage solutions.
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