Nexus MI300X Training Pod — Multi-Node Cluster (8-Rail 400G Fabric)
Rack-scale MI300X cluster engineered for distributed model training.
We help you choose, configure, and deliver the right system — no obligation.
Configuration at a Glance
Tailored per engagement. Full technical overview below.
Configuration Options
Core specifications for this system. Every component is configurable to your workload — request a quote for a tailored build.
High-throughput parallel filesystem
Overview
The Nexus MI300X Training Pod links multiple 8-GPU MI300X nodes over an 8-rail 400G fabric with shared parallel storage and ROCm-based orchestration, delivered as one engineered system rather than a parts list. Nexus Compute designs, sources, stages, and tests the full pod across compute, fabric, and storage, then delivers it warranty-backed through authorized channels.
Who This Solution Is For
Business Benefits
Designed as one system
Compute, fabric, and storage are specified together so the pod performs as an integrated whole.
Scales by adding nodes
The 8-rail fabric is built so additional MI300X nodes extend the same cluster as demand grows.
Single accountable supplier
Nexus coordinates the multi-vendor build into one engagement with consolidated warranty.
Typical Business Use Cases
Distributed training (FSDP, Megatron, DeepSpeed) on ROCm
Foundation and large custom model training
Shared multi-team research compute
Building an owned AMD AI training platform
Industry Applications
Technical Overview
A multi-node pod of 8-GPU MI300X servers, each node using 4th-gen Infinity Fabric for intra-node all-to-all communication and a dedicated 400G NIC per GPU for scale-out. Nodes connect through an 8-rail optimized fat-tree fabric (RoCEv2 or InfiniBand NDR) backed by a high-throughput parallel filesystem and Slurm or Kubernetes orchestration.
| Compute Nodes | Multiple 8x MI300X servers (192GB HBM3 per GPU) |
| Intra-Node Interconnect | AMD Infinity Fabric, all-to-all per node |
| Cluster Fabric | 8-rail fat tree, 400G RoCEv2 or InfiniBand NDR (1:1 GPU:NIC) |
| Shared Storage | High-throughput parallel filesystem |
| Orchestration | Slurm or Kubernetes with ROCm |
| Scale | 16 to 64+ MI300X GPUs (configurable) |
| Monitoring | GPU, fabric, and job health monitoring |
| Deployment | Design, sourcing, staging, and commissioning support |
Specifications are indicative and configured to each engagement. Request a quote for a configuration tailored to your requirements.
Warranty, Support & Fulfillment
Every system ships from an authorized channel, configured and tested, with the documentation enterprise buyers need — backed by warranty and a dedicated account team.
Enterprise Warranty
Full manufacturer warranty with optional on-site, next-business-day support and extended coverage.
Authorized Channel
Sourced through Tier-1 distribution and OEM partners — never grey market. Asset & warranty records included.
Lead Time & Deployment
48-hour quotes, then configured, burn-in tested, and delivered on a committed schedule.
Nationwide Fulfillment
Coordinated logistics, rack-and-stack, and delivery wherever your infrastructure lives.
Frequently Asked Questions
How do you size the pod?
We size node count, fabric, and storage to your model scale and training timeline. Scoping balances total HBM3 capacity, interconnect bandwidth, and budget against your training objectives.
Why an 8-rail fabric?
An 8-rail optimized fat tree gives each of the eight GPUs per node its own switch path, sustaining full RDMA bandwidth for collectives across nodes. It is the AMD reference approach for MI300X scale-out.
Can the pod grow after initial deployment?
Yes. We design the fabric and power so additional MI300X nodes attach to the same cluster, letting you start viable and expand without re-architecting.
Hardware Assistance
Configure the Nexus MI300X Training Pod — Multi-Node Cluster (8-Rail 400G Fabric) with Nexus Compute
Tell us your requirements and a hardware specialist will help you specify, configure, and quote the right system — typically within two business days. No obligation.