Modal Deploys Nvidia Vera CPU After Tests Show 2x Throughput Gains Over Intel and AMD Chips

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AI cloud provider Modal will deploy Nvidia Vera CPU alongside its existing Intel and AMD processors after internal tests revealed doubled throughput for AI agent workloads. The move marks Modal's first adoption of Arm architecture as the $15.75 billion company builds a heterogeneous server fleet optimized for agentic AI workflows.

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Modal Shifts to Nvidia Vera CPU for AI Agent Workloads

Modal, a cloud AI provider valued at $15.75 billion, announced it will deploy Nvidia Vera CPU for the first time after internal benchmarks showed the chip doubles throughput for AI agents compared to its existing Intel and AMD processors

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. The company provides cloud infrastructure for deploying autonomous AI agents that perform complex operations over extended periods, requiring substantial CPU resources that GPUs cannot efficiently handle. Modal's Sandboxes product, which generates $300 million annually, will run on the Arm architecture-based Vera processors

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The AI startup raised $750 million last week in a funding round led by Accel and expects to reach $1 billion in annual revenue within the next year

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. Modal's service consumption has tripled in the last 12 months while revenue surged more than 15 times year-over-year, reflecting explosive growth in AI agentic workflows

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2x Performance Jump Under Heavy Containerized Workloads

Modal developed "Agentless Agent Bench," a benchmark featuring 50 terminal-based coding tasks including software compilation and file searches, to evaluate Nvidia central processors against x86 chips

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. When run individually, Vera showed modest advantages over Intel, AMD, and Amazon processors. However, the 2x performance jump emerged when Modal packed servers with hundreds of concurrent containerized workloads

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Unlike Intel and AMD chips that hit throughput plateaus under heavy load, Vera maintained consistent task completion speeds as workload density increased

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. Modal attributes this advantage to Vera's high memory bandwidth of 1.2 TB/s and its ability to pack more concurrent workloads per core without CPU throttling

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. The chip's Arm v9.2-based Olympus CPU cores can decode and execute up to 10 instructions per cycle, contributing to its performance characteristics.

Heterogeneous Server Fleet Strategy Balances Performance

Modal will operate a heterogeneous server fleet rather than replacing its x86 chips entirely, according to CEO Erik Bernhardsson

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. The company will continue using Intel and AMD processors alongside Vera, deploying each workload on whatever hardware performs best. This approach acknowledges that transitioning to Arm architecture requires substantial software modifications that cannot happen overnight

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Bernhardsson emphasized that Modal's tests compared Vera against currently available Intel and AMD processors, not upcoming generations that will compete directly with Nvidia's offering

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. The company plans to evaluate next-generation chips from both rivals when they become available, maintaining flexibility to optimize its cloud computing infrastructure.

Nvidia Targets $200 Billion Server CPU Market

Nvidia expects to generate at least $20 billion from Vera chip sales in its current fiscal year, targeting a total available market valued at approximately $200 billion

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. The company is on track to sell at least four million Vera CPUs in fiscal 2026, marking a significant push into the server processor market long dominated by Intel and AMD.

OpenAI, Anthropic, Oracle, and Perplexity have also committed to using Vera processors for AI agent sandboxes and related workloads

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. Perplexity reported 1.5 times faster coding tasks when testing the chip. The adoption by Modal represents another validation of Nvidia's strategy to capture share in AI infrastructure beyond its dominant position in GPU accelerators.

AI Agent Workloads Drive CPU Architecture Shifts

AI agent sandboxes present unique computational challenges that favor Vera's architecture

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. These isolated environments where AI agents execute self-generated code exhibit unpredictable resource consumption patterns. An agent may remain idle while waiting for model responses, then suddenly demand ten times more computing power during software installations. Hundreds of these run simultaneously on each server, creating contention for CPU cores, cache capacity, and memory bandwidth

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Modal identified two critical use cases where Vera's advantages matter most: background coding agents and reinforcement learning workloads where expensive GPUs sit idle until CPU processing completes

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. As AI agentic workflows become more prevalent, the shift toward processors optimized for these specific workload characteristics may accelerate across cloud computing platforms.

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