Demo Stage Agenda

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. 

Beyond Benchmarks to Real Products:
Automatically convert trained AI models into optimized mobile apps, ready to deploy within hours—no app development required.

End-to-End Automated Pipeline:
Seamlessly perform AI model conversion, device-specific optimization (NPU/GPU/CPU), and automatic SDK and app template generation (Android, iOS, Flutter).

Real-World Impact & Use Cases:
Proven deployments across industries including healthcare and enterprise, delivering immediate value through reduced costs, enhanced data security, and superior real-time performance.

As AI accelerates, the data center’s optical infrastructure faces unprecedented demands. To support this transformation, we knew that connectivity as usual wouldn’t cut it and explored a new dimension of fiber innovation, Spreadable Adapter Technology. This patented breakthrough streamlines installations, enhances port access, and reduces accidental disconnects, optimizing uptime in dense AI environments. Faster moves, adds, and changes minimize friction, saving time and reducing costs. As AI pushes data centers further and faster, Spreadable Adapters are redefining efficiencies at the physical layer. We’ll share how this technology is shaping AI networks and unlocking new possibilities for scalable, high-performance infrastructure solutions.

Generative AI is fundamentally changing how datacenters are built, putting three types of silicon center-stage: GPUs, custom AI ASICs, and advanced networking processors. Driven by these technologies, the datacenter processor market soared to $147 billion in 2024 and is expected to double by 2030, largely thanks to explosive growth in GPUs and specialized AI ASICs.

While GPUs remain the reference for AI training and inference, hyperscale providers, eager to reduce their dependence on Nvidia, are increasingly co-designing specialized AI ASICs with chipmakers like Broadcom, Marvell, and Alchip. These ASICs sacrifice some versatility to achieve superior performance and energy efficiency, creating opportunities for a thriving startup scene featuring companies like Groq, Cerebras, and Tenstorrent, and spurring major waves of venture investment and mergers. Crucially, chiplet architectures, which combine multiple smaller chip components into a single, optimized package, are now key to driving GPU and ASIC performance upward, beyond what traditional single-chip designs can deliver.

As AI models become ever larger and require responses within milliseconds, networking silicon has become just as critical as processors themselves. DPUs, smart network cards, and advanced switches now coordinate massive arrays of accelerators, making both scale-up and scale-out networks a pivotal part of datacenter performance.