RoCE

RDMA over Converged Ethernet (RoCE v2)

The fabric connecting our AMD Instinct and NVIDIA GPU clusters

Overview

RoCE v2 is the default interconnect for our AMD Instinct MI350X, MI300X and MI325X instances, as well as for VM and Container form factors where customers need RDMA performance without a separate InfiniBand fabric. All GPU models on our platform — across Bare Metal, Virtual Machines and Containers — can be connected via RoCE, with Managed Slurm and Kubernetes scheduling jobs directly onto RoCE-connected GPU pools.

Key specifications

Bandwidth
800 Gbps per port
Latency
<2 µs port-to-port
Topology
Spine-leaf with full bisection
Congestion Control
PFC + ECN + DCQCN
Collectives
NCCL (NVIDIA) / RCCL (AMD)
Available form factors
Bare Metal, VM, Container

Core advantages

Connects every GPU on our platform

AMD MI350X, MI300X, MI325X, NVIDIA H200 and RTX PRO 6000 all ship with RoCE-capable NICs. Bare Metal, VM and Container instances can be mixed on the same RoCE fabric without re-cabling.

Integrated with Managed Slurm

Our Managed Slurm service places training jobs with RoCE topology awareness — NCCL and RCCL collectives run natively over RoCE, with per-job bandwidth monitoring built into the scheduler dashboard.

Integrated with Kubernetes Service

Kubernetes pods on TKS get RoCE-aware pod placement via the GPU operator. Volcano gang scheduling ensures distributed training pods land on the same RoCE fabric segment for low-latency AllReduce.

800G per port across all data centres

Glasgow, London and Texas data centres all run 800G RoCE spine-leaf fabrics with full bisection bandwidth — the same fabric that serves our AI Token Foundry, Intelligent Automation and Bioinformatics solutions.

Ideal for

Bare Metal 8-way MI350X or MI300X training nodes (AMD ROCm with RCCL)
Virtual Machine and Container clusters mixing AMD and NVIDIA GPUs
Managed Slurm distributed training with NCCL/RCCL collectives
Kubernetes inference fleets serving multiple tenants on shared fabric
AI Token Foundry deployments routing across multi-vendor GPU pools

Ready to put Technova to work?

Talk to our team about a custom GPU cluster, managed Slurm or one of our vertical AI solutions.