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Performance matters too. @HPE's Private Cloud AI (the turnkey AI factory in HPE AI Factory with NVIDIA portfolio) and @NVIDIA accelerated compute, GPU-direct RDMA memory access via Alletra X10000. Result: 20x performance improvement on agent memory operations. Agents run on the same box as inferencing. No cloud roundtrip. Data can remain on premises, which for enterprises with strict data sovereignty requirements isn't just a nice-to-have, it's the entire baseline.
Most enterprises focus on the model. But agents need three things to run: → A runtime environment (@HPE uses @NVIDIA's OpenShell, Kubernetes-based) → Model inferencing, ideally on the same box → Fast, persistent memory the agent can read and write continuously Miss any one of these and it becomes much harder to build a true agentic system. Instead, you may end up with something closer to an expensive chatbot.
Security in agentic AI is a different problem than traditional IT security. @HPE's approach with @NVIDIA's NeMo architecture: → Certified skill catalogs: every MCP tool is vetted before deployment → Agents never hold passwords, credentials are issued per-task, then revoked → Agent-level sandboxing so you can reset one agent without taking down the whole system Granular control. Not kill-switch control.
If your org is planning an agentic AI deployment, the infrastructure conversation needs to happen before the model conversation. Governance, resilience, security, and performance are not software features. They're architectural decisions. Watch the full conversation with @HPE and @NVIDIA to see how Private Cloud AI (the turnkey AI factory in HPE AI Factory with NVIDIA portfolio) is built for this. What's the biggest infrastructure concern holding your org back from agentic AI? Reply below. Learn More: https://www.hpe.com/us/en/solutions/artificial-intelligence/nvidia-collaboration.html?utm_campaign=FY26_Q3_AI_GB_GD_WW_WW_Speed_Time_to_AI_Value&utm_medium=DD&utm_source=HIP&utm_content=521229218&crid=%ecid!&plid=%epid
Agentic AI doesn't just run queries. It sets goals, calls tools, accesses systems, and makes decisions autonomously. That changes how enterprises think about infrastructure. I talked to Sadeepa Wijesekara, VP of Engineering at @HPE, about how @NVIDIA's AI software stack helps support secure, enterprise-ready Agentic AI at HPE Discover. Here's the breakdown... #HPEPartner #AgenticAI
Performance matters too. @HPE's Private Cloud AI (the turnkey AI factory in HPE AI Factory with NVIDIA portfolio) and @NVIDIA accelerated compute, GPU-direct RDMA memory access via Alletra X10000. Result: 20x performance improvement on agent memory operations. Agents run on the same box as inferencing. No cloud roundtrip. Data can remain on premises, which for enterprises with strict data sovereignty requirements isn't just a nice-to-have, it's the entire baseline.
Most enterprises focus on the model. But agents need three things to run: → A runtime environment (@HPE uses @NVIDIA's OpenShell, Kubernetes-based) → Model inferencing, ideally on the same box → Fast, persistent memory the agent can read and write continuously Miss any one of these and it becomes much harder to build a true agentic system. Instead, you may end up with something closer to an expensive chatbot.
Security in agentic AI is a different problem than traditional IT security. @HPE's approach with @NVIDIA's NeMo architecture: → Certified skill catalogs: every MCP tool is vetted before deployment → Agents never hold passwords, credentials are issued per-task, then revoked → Agent-level sandboxing so you can reset one agent without taking down the whole system Granular control. Not kill-switch control.
If your org is planning an agentic AI deployment, the infrastructure conversation needs to happen before the model conversation. Governance, resilience, security, and performance are not software features. They're architectural decisions. Watch the full conversation with @HPE and @NVIDIA to see how Private Cloud AI (the turnkey AI factory in HPE AI Factory with NVIDIA portfolio) is built for this. What's the biggest infrastructure concern holding your org back from agentic AI? Reply below. Learn More: https://www.hpe.com/us/en/solutions/artificial-intelligence/nvidia-collaboration.html?utm_campaign=FY26_Q3_AI_GB_GD_WW_WW_Speed_Time_to_AI_Value&utm_medium=DD&utm_source=HIP&utm_content=521229218&crid=%ecid!&plid=%epid
Agentic AI doesn't just run queries. It sets goals, calls tools, accesses systems, and makes decisions autonomously. That changes how enterprises think about infrastructure. I talked to Sadeepa Wijesekara, VP of Engineering at @HPE, about how @NVIDIA's AI software stack helps support secure, enterprise-ready Agentic AI at HPE Discover. Here's the breakdown... #HPEPartner #AgenticAI
Guardrails removed spam, off-topic, unclear, or duplicate replies.
Ask a question below.
Published answers will appear here.