{"id":85960,"date":"2026-03-25T09:58:19","date_gmt":"2026-03-25T07:58:19","guid":{"rendered":"https:\/\/gulftech-news.com\/en\/?p=85960"},"modified":"2026-03-25T09:58:20","modified_gmt":"2026-03-25T07:58:20","slug":"f5-and-nvidia-advance-ai-factory-economics-with-new-capabilities-for-accelerated-ai-inference","status":"publish","type":"post","link":"https:\/\/gulftech-news.com\/en\/2026\/03\/25\/f5-and-nvidia-advance-ai-factory-economics-with-new-capabilities-for-accelerated-ai-inference\/","title":{"rendered":"F5 and NVIDIA advance AI factory economics with new capabilities for accelerated AI inference"},"content":{"rendered":"\n

F5 BIG-IP Next for Kubernetes accelerated with BlueField DPUs improves token throughput, reduces cost per token, and enables secure multi-tenant AI infrastructure, transforming AI factories for the agentic era<\/strong><\/p>\n\n\n\n

F5<\/a><\/strong> (NASDAQ: FFIV), the global leader in delivering and securing every app and API, today announced expanded capabilities in its ongoing\u00a0collaboration<\/strong><\/a>\u00a0with\u00a0NVIDIA\u00a0to accelerate and optimize AI inference infrastructures.<\/p>\n\n\n\n

The expanded integration combines F5 BIG-IP Next for Kubernetes<\/strong><\/a> with NVIDIA BlueField-3 DPUs, creating an intelligent, telemetry-aware infrastructure layer that increases token throughput with better GPU utilization, reduces latency, and enables secure multi-tenant AI platforms at scale.<\/p>\n\n\n\n

In AI systems, tokens represent the measurable unit of AI output\u2014the words, symbols, or data fragments generated and processed during inference. The volume and velocity of token production ultimately determine user experience, infrastructure efficiency, and revenue per accelerator.<\/p>\n\n\n\n

As enterprises and GPUaaS providers race to monetize AI and move from AI experimentation to revenue-generating services, infrastructure efficiency has become a defining metric. Success is increasingly measured not simply by deployed GPU capacity, but by token economics, sustained token throughput, time to first token (TTFT), cost per token, and revenue per GPU accelerator. The F5 and NVIDIA joint solution is designed to directly address these metrics.<\/p>\n\n\n\n

Optimizing tokenomics through intelligent AI infrastructure<\/strong><\/p>\n\n\n\n

The shift from application-centric inference to agent-driven AI workflows demands new architectural approaches to optimize token throughput and reduce costs. BIG-IP Next for Kubernetes now leverages NVIDIA NIM statistics, Dynamo runtime signals, and GPU telemetry to make inference-aware routing decisions before execution. By matching workloads to the most appropriate accelerators in real time, the solution increases sustained utilization while reducing latency and re-compute.<\/p>\n\n\n\n

\u201cAI infrastructure is no longer just about access to GPU or scaling their deployments. It has evolved into maximizing economic output per accelerator,\u201d said Kunal Anand, Chief Product Officer, F5. \u201cTogether with NVIDIA, we are enabling AI factories to treat token production as a measurable business metric. BIG-IP Next for Kubernetes provides the intelligence and governance required to increase GPU yield, reduce cost per token, and scale shared AI platforms confidently.\u201d<\/p>\n\n\n\n

Validated infrastructure efficiency: A structural uplift<\/strong><\/p>\n\n\n\n

The performance numbers speak for themselves. In testing validated by The Tolly Group, BIG-IP Next for Kubernetes, accelerated by NVIDIA BlueField-3 DPUs, delivered up to a 40% increase in token throughput, a 61% faster time to first token (TTFT), and a 34% reduction in overall request latency.<\/p>\n\n\n\n

These are not incremental gains. By offloading networking, TLS\/encryption, AI-aware load balancing, and traffic management to NVIDIA BlueField-3 DPUs, BIG-IP Next for Kubernetes preserves host CPU capacity and frees GPUs to do what they were built for: sustained, high-throughput inference at scale. <\/p>\n\n\n\n

The result is improved GPU utilization, reduced queuing delays, and increased token yield\u2014enabling lower cost per token within a fixed infrastructure footprint. Critically, no model modifications were required, making these gains immediately deployable across existing AI factory infrastructure. For enterprises and NeoCloud providers competing on token economics, this is the difference between infrastructure that constrains AI output and infrastructure that accelerates it.<\/p>\n\n\n\n

\u201cNVIDIA\u2019s accelerated computing infrastructure coupled with F5\u2019s AI-aware Application Delivery and Security Platform unlocks superior AI factory tokenomics\u2014delivering scalable and cost-effective inference without making any changes to the models,\u201d said Kevin Deierling, SVP, Networking, NVIDIA. \u201cTogether, F5 and NVIDIA are empowering enterprises to scale AI factory inference efficiently and economically.\u201d<\/p>\n\n\n\n

Built for agent-driven AI and multi-tenant AI platforms<\/strong><\/p>\n\n\n\n

Modern AI workloads are increasingly agent-driven, persistent, and context-aware. They demand intelligent traffic control that traditional load balancing cannot provide. The enhanced BIG-IP Next for Kubernetes solution can now support:<\/p>\n\n\n\n