Shivay Lamba

Securely database connections in MCP based Agentic applications has become a critical challenge. Traditional approaches for connectivity introduce risks such as prompt injection, over-permissioned access, and lack of observability. This talk introduces the Model Context Protocol (MCP) Toolbox for...

Multimodal models like CLIP are typically deployed in the cloud due to their size and computational demands, limiting their use in latency-sensitive, privacy-preserving, and offline-first applications. This talk demonstrates how one can run fully on-device CLIP inference on Android using ExecuTorch...

A Tale of Two DSLs: A Comparative Study of vLLM GPU Performance with cuTile and CuTe DSL - Anil Vishnoi & Matthew Odden, Red Hat Bringing BitNet to ExecuTorch via Vulkan - Marcus Edel & Vineet Suryan, Collabora Building Production-Grade PyTorch Inference Pipelines for 100K+ Heterogenous Devices -...

Edge computing is becoming increasingly popular for applications that require low latency and high bandwidth. However, building machine learning runtimes for edge computing infra that is both scalable and optimized can be challenging. In this talk, we explore how PyTorch and WebAssembly (Wasm) can...

On-Device Training with ONNX Runtime for PyTorch Models - Kshama Pawar, Microsoft Corporation On-Device Training is a new capability in ONNX Runtime to train models on the edge for Federated Learning and Personalization scenarios. ONNX Runtime provides an efficient, local trainer, that trains with...