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# NVIDIA Vera CPU Boosts AI Factory Throughput to Accelerate Agentic Workloads

**[NVIDIA Developer](https://daily.dev/sources/nvidiadev)** · 7 min read · 0 upvotes · 0 comments

## Summary

NVIDIA's Vera CPU is designed to address CPU-side bottlenecks in agentic AI workloads running across AI factories. The post explains three key ways a slow CPU hurts GPU fleet performance: degraded reinforcement learning feedback quality, higher per-user latency, and increased KV-cache eviction. Vera CPU addresses these with its Olympus core architecture featuring a neural branch predictor, 10-wide decode front end, deep out-of-order execution, and NVIDIA Spatial Multithreading. Key specs include 88 monolithic cores, 40% lower peak loaded latency versus x86, and up to 1.2 TB/s memory bandwidth (14 GB/s per core at less than half the power of traditional data center CPUs). The result is faster RL training convergence, more predictable agent response times, and better GPU utilization through reduced KV-cache eviction.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://developer.nvidia.com/blog/nvidia-vera-cpu-boosts-ai-factory-throughput-to-accelerate-agentic-workloads>

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---

Tags: [#hardware](https://daily.dev/tags/hardware), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning), [#agentic-ai](https://daily.dev/tags/agentic-ai), [#ai-inference](https://daily.dev/tags/ai-inference)

[View this post on daily.dev](https://daily.dev/posts/nvidia-vera-cpu-boosts-ai-factory-throughput-to-accelerate-agentic-workloads-dddu1jnyf)

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