Picture a platform engineer opening the cluster dashboard on a Monday.
Every GPU is booked, and the waitlist is longer than it was last month.
The slowdown rarely comes from the models themselves.
Every inference request also carries routing decisions and a security pass, and on most clusters that work runs on the same cores that should be generating tokens.
That is the setup Patrick Kennedy described on ServeTheHome this month, after touring an F5 lab in California.
F5's BIG-IP Next for Kubernetes put the LLM routing, token governance, and L4 to L7 security on NVIDIA BlueField-3 DPUs, which leaves the host CPUs and the GPUs on inference.
The lab reported over 3x the performance from the same GPU cluster, and the underlying point holds: more performance from the same power and the same GPU footprint, which are exactly the two limits teams are working with today.
You do not need a DPU lab to feel that math.
When compute is rented by the hour, wasted GPU time is wasted money.
An RTX 3090 on BHK Cloud runs $0.15/hr, which makes it practical to measure that overhead on a real workload and tune the routing before scaling up.
Buy Now Pay Later keeps it zero upfront, with payment after one month.
In your last deployment, what ate more GPU time: the model, or everything around it?
https://ai.bhkcloud.com/?utm_source=linkedin&utm_medium=social&utm_campaign=bhk_social_2026w41&utm_content=evening
Originally posted on linkedin_personal