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

2026-07-16 · Source: NVIDIA Technical Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Cybersecurity & Data Privacy · Depth: Advanced, medium

Summary

Agentic AI factories, characterized by numerous model and tool calls per request, demand a new infrastructure pattern to efficiently move, protect, retrieve, and reuse data. The NVIDIA BlueField platform, featuring BlueField-4 DPUs and Vera BlueField-4 STX storage processors, addresses these demands by offloading infrastructure work from host CPUs and accelerating data movement. Combined with NVIDIA DOCA software, BlueField-4 DPUs integrate up to 800 Gb/s Ethernet or InfiniBand connectivity, a 64-core NVIDIA Grace CPU, and LPDDR5X memory, offering up to 6x more compute performance, 4x memory capacity, and 3x memory bandwidth compared to BlueField-3. This co-designed approach improves GPU utilization, reduces latency, strengthens isolation, lowers cost per token, and increases tokens per watt within AI factory data paths, making infrastructure an integral part of the inference pipeline.

Key takeaway

For MLOps Engineers scaling agentic AI deployments, you should evaluate NVIDIA BlueField-4 DPUs and Vera BlueField-4 STX storage processors. Integrating these with NVIDIA DOCA can significantly improve GPU utilization and reduce inference latency by offloading critical infrastructure tasks. Consider how dedicated infrastructure processing can secure multi-tenant environments and optimize KV cache management, directly impacting your cost per token and overall power efficiency for long-context workloads.

Key insights

Agentic AI integrates infrastructure directly into the inference pipeline, requiring extreme co-design for optimal performance.

Principles

• Infrastructure processing must be offloaded from host CPUs.
• Context memory, like KV cache, is an active infrastructure data path.
• In-silicon security strengthens protection outside host workload domains.

Method

NVIDIA BlueField-4 DPUs and Vera BlueField-4 STX storage processors, powered by NVIDIA DOCA, offload, accelerate, and isolate networking, storage, security, telemetry, and control-plane services directly in the AI factory data path.

In practice

• Use DOCA Memos to manage and share KV cache across compute and storage nodes.
• Implement BlueField ASTRA for Spectrum-X zero-trust, multi-tenant bare-metal deployments.
• Leverage DOCA security services for inline policy enforcement and isolation.

Topics

• Agentic AI
• NVIDIA BlueField
• DPU
• NVIDIA DOCA
• KV Cache Management
• AI Factory Infrastructure
• Zero-Trust Security

Code references

• NVIDIA-DOCA/doca-samples

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Architect, MLOps Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA Technical Blog.