InfoQ - GPU - Presentations https://www.infoq.com InfoQ GPU Presentations feed Presentation: From S3 to GPU in One Copy: Rethinking Data Loading for ML Training https://www.infoq.com/presentations/vortex-columnar-file-format-gpu-streaming/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=GPU-presentations <img src="https://res.infoq.com/presentations/vortex-columnar-file-format-gpu-streaming/en/mediumimage/onur-satici-medium-1787813397611.jpeg"/><p>Onur Satici explains how Vortex, an open-source columnar file format under the Linux Foundation, revolutionizes high-throughput data loading. He details how cascading lightweight encodings, layout-based segment pruning, and zero-copy memory pipelines eliminate CPU/NVMe bottlenecks to stream S3 data straight to GPUs at speeds up to 60 Gbps without requiring upfront data reprocessing.</p> <i>By Onur Satici</i> Architecture Columnar Databases Data Lake GPU Machine Learning S3 Streaming Transcripts QCon London 2026 Data Pipelines CUDA Rust Performance AI, ML & Data Engineering Architecture & Design Development presentation Fri, 04 Sep 2026 11:00:00 GMT https://www.infoq.com/presentations/vortex-columnar-file-format-gpu-streaming/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=GPU-presentations Onur Satici 2026-09-04T11:00:00Z /presentations/vortex-columnar-file-format-gpu-streaming/en Presentation: Running AI at the Edge: Running Real Workloads Directly in the Browser https://www.infoq.com/presentations/local-ai-browser-inference-privacy/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=GPU-presentations <img src="https://res.infoq.com/presentations/local-ai-browser-inference-privacy/en/mediumimage/james-hall-medium-1787813225372.jpeg"/><p>James Hall discusses the strategic and technical imperative of moving AI workloads from cloud providers to local edge devices. He shares practical approaches using WebGPU, Transformers.js, and DuckDB to achieve near-native performance in JavaScript. Through real-world case studies, he explains how to minimize data privacy risks, optimize browser inference, and build rigorous evaluation suites.</p> <i>By James Hall</i> Transcripts Edge Computing QCon London 2026 AI Security Cloud Computing GPU Web Browser Machine Learning Local Inference Web Development Privacy AI, ML & Data Engineering Architecture & Design Development presentation Mon, 31 Aug 2026 11:00:00 GMT https://www.infoq.com/presentations/local-ai-browser-inference-privacy/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=GPU-presentations James Hall 2026-08-31T11:00:00Z /presentations/local-ai-browser-inference-privacy/en