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Rebellions Product Demo

Rebellions

Website: https://rebellions.ai/

Rebellions develops and mass-produces AI accelerators optimized for Large Language Models, Multi-Modal Models, and scale-out inference workloads, delivering industry-leading energy efficiency, scalability, and deployment readiness. Its flagship REBEL chip features a modular chiplet architecture, UCIe connectivity, 144GB HBM3E memory, and REBEL-IO for hyperscale, exa-scale AI inference clusters.

The platform is supported by a mature software stack designed for seamless integration with existing datacenter infrastructure and popular AI frameworks. This builds on the proven mass-production experience of ATOM, launched in 2023 and deployed in production data centers.

Backed by SK Telecom, SK hynix, Aramco’s Wa’ed Ventures, and KT, and strengthened by its merger with SK SAPEON, Rebellions is positioned as Asia’s leading independent AI semiconductor platform for sovereign AI and next-generation datacenter deployments.

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MLCommons - MLPerf Inference Results

Following the MLCommons Q3 MLPerf Inference results announcement on the morning of Tuesday 9th September on the keynote stage, MLCommons Founder & Executive Director David Kantar will deliver a detailed analysis of the results followed by a Q&A session from the audience.

Author:

David Kanter

Founder & Executive Director

MLCommons

David co-founded and is the Head of MLPerf for MLCommons, the world leader in building benchmarks for AI. MLCommons is an open engineering consortium with a mission to make AI better for everyone through benchmarks and data. The foundation for MLCommons began with the MLPerf benchmarks in 2018, which rapidly scaled as a set of industry metrics to measure machine learning performance and promote transparency of machine learning techniques. In collaboration with its 125+ members, global technology providers, academics, and researchers, MLCommons is focused on collaborative engineering work that builds tools for the entire AI industry through benchmarks and metrics, public datasets, and measurements for AI Safety. Our software projects are generally available under the Apache 2.0 license and our datasets generally use CC-BY 4.0.

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Google Product Demo

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Google Deepmind - Building Effective Agents

Large language models can now power capable software agents, yet real‑world success comes from disciplined engineering rather than flashy frameworks. Most reliable agents are built from simple, composable patterns instead of heavy abstractions.

The talk will introduce patterns to add complexity and autonomy only when it pays off. Attendees should leave with a practical decision framework for escalating from a single prompt to multi‑step agents, also keeping in mind guardrails for shipping trustworthy, cost‑effective agents at scale.