Arm

AI infrastructure is forcing the industry to rethink how compute is designed, deployed and scaled across the data center. That's why Meta chose Arm. At #OCPBarcelona26, Paul Saab shared how quick it was to port Meta's foundational software that runs everything from Messenger, Instagram and now Meta AI to Arm - with just 5 engineers in 3 months. That speed matters. It proves modern AI infrastructure can transition to Arm's efficient compute architectures without massive rewrites or operational drag. Today, we continue to work closely together, launching the Arm AGI CPU back in March as part of Meta's pursuit for personal super intelligence — built on a compute platform focused on performance-per-watt, software portability, and scalable efficiency. ⚡️ https://okt.to/DzWl6x

Code-Along incoming! Designed to help developers move from low-level metrics to actionable optimization insights across modern software pipelines - this is your chance for hands-on demos with Arm engineers on how to: 💡Identify code hotspots 💡Interpret profiling data 💡Apply targeted optimizations using Performix workflows and advanced command-line analysis Be sure to join us next week to further explore performance tuning and analysis with Arm Performix. 🔗: https://okt.to/lpwJ4X

Counting down to #COMPUTEX2026. ⏳ Tune in live from Taipei on June 2 as Rene Haas shares how Arm is enabling agentic AI from cloud to edge. From hyperscale cloud to AI PCs and intelligent edge devices, we’re enabling AI at every scale, delivering the performance, efficiency and flexibility required across the full compute spectrum. https://okt.to/MgBfHe

AI is moving from experimentation into deployment across devices, enterprise and cloud. Performance and efficiency remain critical—they’re the foundation for AI at scale, and the pressure on both only increases as workloads grow. Scaling AI introduces a new set of challenges: coordinating workloads, managing complexity across environments, and making systems usable for developers. The Arm compute platform is built for this next phase of AI, enabling performance, efficiency and scalability from edge to cloud.

At #WebSummitVancouver, Arm CMO Ami Badani discussed why the rise of AI agents is reshaping how the industry thinks about compute infrastructure. Agentic AI is moving from centralized training environments ➡️ into real-world deployment - making the role of the CPU more important than ever. Scale across cloud and edge environments, calls for increased orchestration and power efficiency - and while GPUs remain essential for training large models - CPUs are playing a key role in managing distributed AI workloads and enabling more efficient compute at scale. Thank you to Web Summit for hosting yet another year of innovative and inspiring conversation. https://okt.to/27aIyv

Last week at the Special Competitive Studies Project - SCSP AI+ Expo, Arm joined industry leaders to discuss the future of AI and semiconductor growth. Vince Jesaitis, VP and Head of Global Government Affairs at Arm, moderated a panel on scaling the physical AI stack, while Stephen Ozoigbo alongside the Virginia Alliance for Semiconductor Technology explored how regional partnerships strengthen the semiconductor talent pipeline for the AI era. As AI moves into real-world systems, continued collaboration across industry, academia and government is critical in accelerating innovation and long-term growth - with Arm providing the trusted compute platform that helps enable them.

Big moments are made possible by the teams who build them. 💙 Behind the Arm AGI CPU was a global group of engineers, researchers, product teams and creatives bringing together deep technical expertise and a shared vision for where AI is headed next. It's a milestone moment for all of the teams who played a role in building the future of AI, on Arm.

On-device AI has often meant trade-offs—performance vs. complexity. That’s what’s changing with Google AI Edge and Arm SME2. With KleidiAI acceleration built into LiteRT, developers can run advanced AI on-device—no code changes required. Case in point: the original FP32 Stable Audio Open Small model runs fully on-device, with over 2x faster generation and 4x lower memory, without sacrificing quality. Start building with Google AI Edge tools and deploy optimized models with KleidiAI-accelerated LiteRT today: https://okt.to/DLqV7t

AI is reshaping semiconductor design, and the infrastructure behind it matters more than ever. 🎥 At #CadenceLIVE, Anirudh Devgan, CEO of Cadence discusses the rise of Arm-based compute across the cloud and data center ecosystem, and why broad software support across architectures is critical for the future of AI-driven design flows. Together, we’re helping enable scalable, AI-driven chip design workflows across modern compute environments. 🤝

We’re excited to host a developer meetup with NVIDIA at our San Jose office on May 19, bringing together developers, AI practitioners, architects, founders, and technical leaders for conversations around the future of AI compute and intelligent systems.👨💻 The evening will feature a panel discussion on building fast, efficient, production-ready AI applications, covering optimization strategies, quantization techniques, frameworks, and architectural considerations for modern AI workloads. Joining the panel and hosts: ➡️ Gene Chorba , Director of Developer Strategy, Arm ➡️ Prashant Sharma, Head of Developer Relations, Arm ➡️ Laurence Moroney, Director of AI, Arm ➡️ Mitesh Patel, Ph.D., Developer Advocate, NVIDIA Attendees can also expect live demos, networking opportunities, conversations with the broader AI developer community, and a raffle with prizes during the event. Space is limited! Register here: https://okt.to/dVme7w