Aug 07, 2026

Compression at the Edge — Chris Alexiuk, NVIDIA

This panel discussion explores the critical role of model compression, particularly quantization, in democratizing AI. It delves into how massive models like GLM 5.2 can be shrunk by over 80% without equivalent performance loss, thanks to techniques like mixed-precision quantization and understanding uneven layer importance. The discussion covers NVIDIA's NVFP4 format, challenges posed by new model architectures, the preference for KL divergence over accuracy benchmarks, and the vision of future AI running efficiently on all local devices.

Aug 05, 2026

Building the First Data Centers in Space

Philip Johnston, co-founder and CEO of StarCloud, discusses their pioneering efforts to build data centers in space to address AI's energy demands and terrestrial constraints. He details StarCloud-1's groundbreaking mission, which launched an Nvidia H100 GPU into orbit, and the engineering challenges overcome, such as thermal management and radiation hardening. The conversation covers their ambitious plan for a 88,000-satellite constellation, the shift in investor sentiment towards hard tech, and key advice for deep tech founders.

Jul 25, 2026

Why Large? Tiny LMs & Agents on Edge/Robotics — Cormac Brick, Google

Cormac Brick from Google AI Edge discusses how the increasing constraint of DRAM cost on edge devices necessitates the development and deployment of increasingly smaller AI models. He outlines the work of his team in optimizing models like Gemma, achieving 2.9 bits per weight for a 2 billion parameter model capable of running on a Raspberry Pi at 7.6 tokens/second, or on an NPU at 31 tokens/second decode for vision tasks. The talk delves into 'tiny models' (50M-500M parameters) that extend AI to older devices and enable features like robust voice-to-function calling via fine-tuning with synthetic data, exemplified by an offline voice dictation app.

Jul 23, 2026

Local Agentic Theory For Mobile Games — Shafik Quoraishee & Joanne Song, The New York Times

This presentation explores the innovative concept of running agentic AI entirely on mobile devices to enhance game accessibility and personalization. It delves into the technical challenges of local AI (space, time, energy budgets), contrasts agentic systems with traditional reinforcement learning, and demonstrates practical applications with a Space Invaders agent and a crossword solver. A key takeaway is the transformation of accessibility from fixed toggles to dynamic, real-time adjustments based on player needs, ultimately envisioning a future of billions of small, personalized local AI brains.

Jul 11, 2026

State of the Union: Why Local, Why Now — NVIDIA, Osmantic, Roboflow, EXO Labs, @matthew_berman

This panel discussion explores the inflection point of Local AI, driven by advanced models, improved hardware, and a robust ecosystem. Experts discuss how this shift addresses critical concerns around privacy, cost, sovereignty, and resilience, emphasizing the pivotal role of open-source AI and specialized models. They delve into technical optimizations, the evolution from generalized to specialized AI, and the challenges of making local AI accessible and performant for both enterprise and individual users.

Jun 30, 2026

Session on Reasoning

This session features two talks on optimizing and verifying AI reasoning. Hongxiang Fan discusses cross-stack co-design for efficient AI, focusing on Test-Time Scaling (TTS) challenges, optimal verification granularity, and system-level optimizations for edge deployments. Nagarajan Natarajan introduces 'Advancing Verified Reasoning' with the InterVent platform, aiming to ensure AI agents comply with complex policies through formal verification, dynamic steering, and leveraging verification signals for training. Both emphasize addressing the computational and reliability costs of advanced AI.