As enterprises scale up their Generative AI (GenAI) ambitions, performance bottlenecks and spiralling infrastructure costs have become key concerns. The solution? End-to-end optimisation—from hardware accelerators to software frameworks and application design.
To decode this, AIM and Intel are presenting the third session in their GenAI webinar series: “All-Round Optimisation for Generative AI.”
Register Now!
📅 Date: 26th June 2025
🕔 Time: 5 – 6 PM IST
The session will explore how to harness hardware accelerators like Intel® Advanced Matrix Extensions (Intel® AMX) in Intel® Xeon® processors, NPU in Intel AI PCs, and Intel® Gaudi® AI accelerators—combined with advanced software frameworks—to drive significant speed-ups in GenAI tasks while lowering Total Cost of Ownership (TCO).
Led by Anish Kumar, AI Software Engineering Evangelist – APAC-s at Intel, this session is designed to equip AI/ML developers with the latest techniques to maximise performance and efficiency for GenAI workloads.
With over 20 years of experience in software engineering, Anish has been instrumental in enabling enterprises across the Asia Pacific region to accelerate AI adoption through engineering-led innovation.
Why Should You Attend?
Participants will gain a holistic understanding of optimisation strategies across the hardware, framework, and application layers, along with practical insights that help reduce power consumption, boost productivity, and shorten time to market.
Key takeaways
- Unique needs of GenAI workloads
- Need for Optimising GenAI
- Optimisation Strategies for Hardware, Framework and Applications layer of GenAI
- Insights into the latest hardware acceleration technologies
- Strategies for reducing TCO and improving productivity
- Opportunities to network with experts and peers in the field
The hands-on workshop would benefit AI/ML developers, architects, and enterprise tech teams looking to scale GenAI efficiently and cost-effectively.
Don’t miss this opportunity to learn how Intel’s AI ecosystem can help you build smarter, faster, and more sustainable GenAI applications.
