Flagship Discipline
The networks AI runs on.
Training and inference don’t fail on compute — they fail on the fabric between the GPUs. We design east-west networks engineered for the synchronized, loss-intolerant, high- bandwidth GPU-to-GPU traffic that AI clusters generate, for organizations standing up AI capability that has to work.
Why AI breaks ordinary networks
GPU traffic is a different animal.
East-west, not north-south
Enterprise networks are built to move traffic to and from users. AI clusters move enormous volumes sideways — node to node, pod to pod — in tightly synchronized bursts.
Loss is fatal
A dropped packet in a collective operation stalls the whole job. These fabrics must be effectively lossless — RDMA / RoCEv2, tuned flow control, no congestion collapse.
Latency compounds
At scale, microseconds multiply across thousands of synchronized exchanges. Topology and buffering decisions made early decide whether the cluster ever hits its numbers.
What we design
From silicon to topology to handover.
High-bandwidth east-west fabric
High-radix leaf-spine engineered for GPU node-to-node traffic, with port speeds (up to 400/800G where the workload calls for it) sized to your collective-communication patterns — not a generic enterprise template.
Lossless transport design
RDMA / RoCEv2 with PFC, ECN, and congestion control tuned for AI collectives. The fabric stays lossless under the bursts that take ordinary networks down.
Rail-optimized topology
Cluster-aware leaf-spine and rail design that maps to your GPU pods, scheduler, and job shapes — maximizing bisection bandwidth where it actually matters.
Training vs. inference modeling
Capacity and topology decisions modeled separately for training (bandwidth-hungry, bursty) and inference (latency-sensitive, steady) so you don’t overbuild or starve either.
Scale & day-2 planning
Designed to grow — adding pods without re-architecting. Cabling, power, and oversubscription planned so the next expansion is a rollout, not a rebuild.
AI-readiness advisory
We’re consulted to review existing customer networks and deliver recommendations that meet today’s requirements while preparing the path to AI-scale workloads.
The advisory role
Already trusted to review the network before it’s built.
Organizations bring us in to review their current network and tell them the truth: what works, what won’t survive an AI workload, and what to change first. We deliver recommendations that satisfy today’s requirements and tomorrow’s AI demands in the same plan.
When to bring us in
- Standing up a new GPU cluster and need the fabric designed right
- Existing network was built before AI was on the roadmap
- A vendor proposal needs an independent, hands-on second opinion
- You need a roadmap that covers both today and the AI build-out
Get a directional read in two minutes.
Our edge-hosted readiness tool gives you an instant, tailored assessment of where your network stands for AI workloads — then connects you to the architect who can take it further.
+1 (949) 945-2117