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# Scaling Agentic AI Factories Through Extreme Co-Design with NVIDIA BlueField

**[NVIDIA Developer](https://daily.dev/sources/nvidiadev)** · 11 min read · 1 upvotes · 0 comments

## Summary

NVIDIA BlueField-4 DPUs and Vera BlueField-4 STX storage processors are designed to handle the infrastructure demands of agentic AI workloads. As agentic AI turns inference into a distributed workflow spanning GPUs, CPUs, memory, networking, and storage, host CPUs become a bottleneck. BlueField offloads networking, storage, security, telemetry, and KV cache management from host CPUs into dedicated in-silicon processors, keeping GPUs and Vera CPUs focused on model execution and agent orchestration. The DOCA software platform makes these services programmable across the data center. Key capabilities include DOCA Memos for KV cache sharing across compute and storage nodes, BlueField ASTRA for zero-trust multi-tenant deployments, and the NVIDIA CMX context memory platform for scalable KV cache storage. The result is higher GPU utilization, lower cost per token, stronger tenant isolation, and better tokens-per-watt efficiency for production agentic AI factories.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://developer.nvidia.com/blog/scaling-agentic-ai-factories-through-extreme-co-design-with-nvidia-bluefield>

## Questions this post answers

### What are the hardware specs of the NVIDIA BlueField-4 DPU compared to BlueField-3?

The BlueField-4 DPU integrates up to 800 Gb/s Ethernet or InfiniBand connectivity, a 64-core NVIDIA Grace CPU, high-bandwidth LPDDR5X memory, and PCIe Gen6. Compared with BlueField-3, it doubles networking bandwidth, delivers up to 6x more compute performance, 4x memory capacity, and more than 3x memory bandwidth.

_Teams evaluating DPU upgrades for AI infrastructure can track BlueField-4 specs and comparisons on daily.dev._

### What is the Vera BlueField-4 STX storage processor used for in AI factories?

It powers NVIDIA CMX, a context memory storage platform for KV cache, combining the Vera CPU, ConnectX-9 networking, and DOCA Memos software. Rather than presenting flash as block storage, it manages KV metadata, queues, cache recall, pre-staging, tenant policy enforcement, and data protection close to the storage and network path to keep long-context and agentic inference fast.

_Architects designing AI-native storage tiers can follow developments like CMX and BlueField-4 STX on daily.dev._

### What does DOCA Memos do for KV cache in agentic AI systems?

DOCA Memos helps manage and share KV cache across compute and storage nodes so context can be reused across long-context and agentic inference. This reduces repeated prefill computation, context recomputation, GPU idle time, and unnecessary data movement, improving tokens per second and power efficiency for multi-turn and multi-agent workloads.

_Developers optimizing KV cache reuse in agentic pipelines can keep up with tools like DOCA Memos on daily.dev._

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- [NVIDIA’s BlueField-4 STX Aims to Fix the Storage Problem AI Agents Keep Running Into](https://daily.dev/posts/nvidia-s-bluefield-4-stx-aims-to-fix-the-storage-problem-ai-agents-keep-running-into-cskx8emnw) · Security Boulevard · 1 upvotes · 0 comments
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---

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

[View this post on daily.dev](https://daily.dev/posts/scaling-agentic-ai-factories-through-extreme-co-design-with-nvidia-bluefield-keqdn8r6u)

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