F5 is Scaling AI Inference from the Inside Out
The infrastructure renaissance has a catch phrase: let servers serve and inferencing inference.
Back in the early days of technology, I spent years mired in testing and analyzing SSL accelerators. These little cards were designed to address a significant problem that arose from the explosive growth of digital business and commerce; namely that security functions using SSL consumed CPU cycles and were a significant source of performance problems. So, the industry—including F5—developed hardware to offload those functions and let servers serve.
Today we’re seeing the same issues arise with AI—specifically inferencing—and, unironically, we’re seeing the same kind of solutions arise; namely, specialized hardware that lets servers serve and inferencing inference.
Yeah, I’m not sure that’s grammatically correct but let’s go with it for now, shall we? Kthx.
As we’ve pointed out, AI applications are modern applications in their architectural construction. But at the heart of an AI application is inferencing, and that is where AI diverges from “normal” modern applications.
Inferencing in Action
We’ve seen how AI compute complexes are constructed out of banks of CPUs and GPUs. These compute resources have ratios and balances that must be maintained to keep the cluster working efficiently. Every time a CPU can’t keep up, a very expensive GPU sits idle.
You see, only part of an inferencing server’s processing is actually inferencing. A great deal of it is standard web processing of HTTP and API requests. It’s that part of the inferencing service that uses the CPU and often becomes overwhelmed. When that happens, the GPUs are used less and less as the server side of inferencing becomes bogged down trying to handle requests.
That’s probably why 15% of organizations report that less than 50% of their available and purchased GPUs are in use (State of AI Infrastructure at Scale 2024).
Part of the problem here is the use of CPU resources for what should be infrastructure work. Services like traffic management, security operations, and monitoring consume CPU resources, too, and contribute to the load on the overall system. That leads to a reduction in capacity and performance of inferencing servers and leads to less utilization of GPU resources.
Luckily, this infrastructure renaissance is all about conserving CPU resources for inferencing work by offloading infrastructure operations to a new processing unit: the DPU.
Now, the interesting thing about DPUs is that they actually support two different modes. In one, they can offload networking like RDMA over Infiniband or Ethernet. This helps immensely when building out an AI compute complex in which significant amounts of data are going to be flowing, such as training an AI model or scaling out inferencing for a large user base.
But DPUs can also be configured in ‘DPU’ mode. In Kubernetes this makes them show up as a separate node on which functions like application delivery and security can run. This effectively reserves CPU compute for inferencing services by ‘offloading’ the less predictable and more demanding infrastructure workloads to their own node in the cluster. This allows solutions like F5 BIG-IP Next SPK (Service Proxy for Kubernetes) to manage and secure inbound N-S AI requests via API and properly distribute them to the appropriate inferencing service within the AI compute complex.
This approach means organizations can leverage existing knowledge and investments in Kubernetes management of infrastructure because our solution is Kubernetes native. Core, cloud, edge—it doesn’t matter because the operation is at the cluster level and that is consistent across all environments.
It also separates responsibility for managing application delivery and security services, which enables network and security ops teams to handle the infrastructure independent of the AI workloads managed by dev and ML ops teams.
Lastly, leveraging the DPU for application delivery and security better supports the multi-tenancy needs of organizations. This is not just about isolating customer workloads, but model workloads. We know from our research that organizations are already using, on average, 2.9 different models. Being able to manage the use of each via a consistent solution will enable greater confidence in the security and privacy of the data being consumed and generated by each individual model.
This isn’t the first time F5 has worked with NVIDIA DPUs on AI-related use cases. But it is the first time we’ve worked together to develop a solution to help customers of all sizes build out scalable and secure AI compute complexes so they can safely and confidently harness the power of inferencing in any environment and optimize the use of GPU resources, so they aren’t sitting around idle.