5 Fast Facts: Arm’s Role in Shaping AI Infrastructure
As artificial intelligence (AI) becomes central to virtually every layer of the compute stack, the onus is shifting from “who can build a fast chip” to “who can build an efficient, scalable, end-to-end AI platform.” Arm has staked its reputation on not just the cores, but on knitting together silicon, software, tools, and partner ecosystems into something more holistic. In this conversation, we put this thesis to the test with Eddie Ramirez, vice president of Go-to-Market, Infrastructure Business at Arm.
Eddie walks us through how Arm’s approach differs when viewed through the lens of full stack deployment rather than just instruction sets, and why decisions made today in software portability, workload optimization, and partner enablement will echo throughout the AI infrastructure investments of the next decade. Below, he dives into how Arm is enabling AI across data centers, edge environments, and everything in between.
Q1: Arm is central to AI across data center, edge, and devices. From your vantage point, what makes Arm’s approach to AI distinctive at the platform level, not just in cores, but in how partners build and deploy AI services end-to-end?
A1: What sets Arm apart is that we’re enabling entire ecosystems. From data center to edge, Arm provides a common foundation across computing components while giving our partners the flexibility to design silicon optimized for their specific workloads. Beyond the silicon, we’re deeply invested in the software stack and tooling and the developer ecosystem, helping developers get top-tier AI performance. We provide tools like Arm Kleidi, a software library that is integrated with leading ML frameworks, to help developers get the best performance possible on Arm-based systems without needing to rebuild their workflows. This full-stack enablement is what makes our approach unique.
Q2: We are shifting to accelerated compute platforms fueling AI factories in the data center. How does Arm help deliver performance and efficiency as a foundational element in these platforms?
A2: With the amount of data that will pump through AI factories, efficiency is no longer negotiable. The highly power efficient Arm Neoverse platform enables hyperscalers and cloud providers to design for high performance, high-throughput AI workloads without breaking their thermal or power envelopes. That means more compute in the same footprint and more AI delivered at scale.
Q3: Analysts project roughly $1T in AI infrastructure investment by 2030. Where does Arm expect the biggest efficiency gains to come from in that buildout, and what choices today will compound the most value over time?
A3: The most valuable efficiency gains will come from system-level choices like performance-per-watt optimization, workload-specific silicon, and software that’s portable across environments. We’re helping the industry enable greater performance with optimized silicon and giving developers a consistent foundation that scales with them.
Q4: Enterprises and providers are moving toward more tailored silicon. How are Arm Total Design and Neoverse CSS accelerating that shift, and what macro outcomes (i.e. time-to-market, performance-per-watt, cost predictability) matter most to customers?
A4: Time-to-market, performance-per-watt, and cost are consistently the top considerations for companies building specialized silicon. Arm Total Design was designed to address those needs by bringing together the pre-integrated foundation of Neoverse CSS with an ecosystem including IP providers, foundries, and EDA tools, working collaboratively. The Arm Total Design ecosystem helps accelerate partners’ time to market with lower engineering costs and reduced friction.
Q5: How does Arm approach software optimization across environments, and does this include work to drive more efficient workload management?
A5: Arm Neoverse architecture is already the foundation for major hyperscaler platforms like AWS’ Graviton, Google Cloud Axion and Microsoft Cobalt. The wide availability of Arm-based options enables a unified software experience for end customers across clouds, on-prem, and edge. We optimize from the framework level all the way down, allowing developers to build once and deploy efficiently and effectively, regardless of environment. For workload management, we invest in tools that help customers make smarter decisions about where and how workloads can be optimized. For example, the Arm Total Performance tool provides the insights needed to tune for performance, efficiency, and scalability of software workloads running on Arm-based silicon. Our goal is to maximize efficiency across entire systems, not just at the chip level.
As artificial intelligence (AI) becomes central to virtually every layer of the compute stack, the onus is shifting from “who can build a fast chip” to “who can build an efficient, scalable, end-to-end AI platform.” Arm has staked its reputation on not just the cores, but on knitting together silicon, software, tools, and partner ecosystems into something more holistic. In this conversation, we put this thesis to the test with Eddie Ramirez, vice president of Go-to-Market, Infrastructure Business at Arm.
Eddie walks us through how Arm’s approach differs when viewed through the lens of full stack deployment rather than just instruction sets, and why decisions made today in software portability, workload optimization, and partner enablement will echo throughout the AI infrastructure investments of the next decade. Below, he dives into how Arm is enabling AI across data centers, edge environments, and everything in between.
Q1: Arm is central to AI across data center, edge, and devices. From your vantage point, what makes Arm’s approach to AI distinctive at the platform level, not just in cores, but in how partners build and deploy AI services end-to-end?
A1: What sets Arm apart is that we’re enabling entire ecosystems. From data center to edge, Arm provides a common foundation across computing components while giving our partners the flexibility to design silicon optimized for their specific workloads. Beyond the silicon, we’re deeply invested in the software stack and tooling and the developer ecosystem, helping developers get top-tier AI performance. We provide tools like Arm Kleidi, a software library that is integrated with leading ML frameworks, to help developers get the best performance possible on Arm-based systems without needing to rebuild their workflows. This full-stack enablement is what makes our approach unique.
Q2: We are shifting to accelerated compute platforms fueling AI factories in the data center. How does Arm help deliver performance and efficiency as a foundational element in these platforms?
A2: With the amount of data that will pump through AI factories, efficiency is no longer negotiable. The highly power efficient Arm Neoverse platform enables hyperscalers and cloud providers to design for high performance, high-throughput AI workloads without breaking their thermal or power envelopes. That means more compute in the same footprint and more AI delivered at scale.
Q3: Analysts project roughly $1T in AI infrastructure investment by 2030. Where does Arm expect the biggest efficiency gains to come from in that buildout, and what choices today will compound the most value over time?
A3: The most valuable efficiency gains will come from system-level choices like performance-per-watt optimization, workload-specific silicon, and software that’s portable across environments. We’re helping the industry enable greater performance with optimized silicon and giving developers a consistent foundation that scales with them.
Q4: Enterprises and providers are moving toward more tailored silicon. How are Arm Total Design and Neoverse CSS accelerating that shift, and what macro outcomes (i.e. time-to-market, performance-per-watt, cost predictability) matter most to customers?
A4: Time-to-market, performance-per-watt, and cost are consistently the top considerations for companies building specialized silicon. Arm Total Design was designed to address those needs by bringing together the pre-integrated foundation of Neoverse CSS with an ecosystem including IP providers, foundries, and EDA tools, working collaboratively. The Arm Total Design ecosystem helps accelerate partners’ time to market with lower engineering costs and reduced friction.
Q5: How does Arm approach software optimization across environments, and does this include work to drive more efficient workload management?
A5: Arm Neoverse architecture is already the foundation for major hyperscaler platforms like AWS’ Graviton, Google Cloud Axion and Microsoft Cobalt. The wide availability of Arm-based options enables a unified software experience for end customers across clouds, on-prem, and edge. We optimize from the framework level all the way down, allowing developers to build once and deploy efficiently and effectively, regardless of environment. For workload management, we invest in tools that help customers make smarter decisions about where and how workloads can be optimized. For example, the Arm Total Performance tool provides the insights needed to tune for performance, efficiency, and scalability of software workloads running on Arm-based silicon. Our goal is to maximize efficiency across entire systems, not just at the chip level.
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