HPE and Kamiwaza rethink AI infrastructure for the inference era
As AI factories evolve into "data centers of the future," the infrastructure stack must also transform into a mix of CPU and GPU platforms that can deliver a full set of AI computing solutions. This runs the gamut from application hosting to intelligence generation and from static workflows to agentic orchestration systems. ] The post HPE and Kamiwaza rethink AI infrastructure for the inference era appeared first on SiliconANGLE.
Key Takeaways
- AI computing for inference speed: HPE and Kamiwaza tackle GPU architecture challenges to deliver production-ready enterprise AI at scale.
- to improve their business," Braun said.
"That's where inference comes in - just trying to use that to get at the underlying understanding of your data is so important.
- ) A new approach to AI computing In response to growing inference demands, HPE has worked with partners such as Kamiwaza and Nvidia Corp.
to improve GPU performance and efficiency in the handling of larger and more complex AI workloads.
- That's extremely complex, and that's extremely limiting because you've now locked that user's session into the GPU.
- The benefits include cost savings and a more environmentally sustainable platform, according to Braun.
AI computing for inference speed: HPE and Kamiwaza tackle GPU architecture challenges to deliver production-ready enterprise AI at scale. UPDATED 12:02 EDT / JUNE 22 2026 AI HPE and Kamiwaza rethink AI infrastructure for the inference era by Mark Albertson As AI factories evolve into "data centers of the future," the infrastructure stack must also transform into a mix of CPU and GPU platforms that can deliver a full set of AI computing solutions. This runs the gamut from application hosting to intelligence generation and from static workflows to agentic orchestration systems.
For key enterprise computing vendors, such as Hewlett Packard Enterprise Co. , it means that organizations increasingly expect production-ready enterprise AI with the governance, security and scale required to move efficiently from pilot to production. The challenge confronting many organizations today is to get beyond the noise surrounding the IT stack and use AI infrastructure to improve inference speed, according to Robin Braun (pictured, left), vice president of AI business development, hybrid cloud, at HPE.
"People are trying to find the signal in the noise; they're trying to use their data to improve their efficiency ... to improve their business," Braun said. "That's where inference comes in - just trying to use that to get at the underlying understanding of your data is so important.
For more details please read the original article at SiliconANGLE AI.
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