A professional two-GPU training, visualisation and applied-research server for larger datasets and models.
This brief explains where the baseline fits, the software and operating model it supports, and the evidence Axiotech should use to validate a final configuration. Indicative specifications remain subject to component availability, export compliance and formal quotation.
Baseline architecture
One 4U node; 96 AMD EPYC cores; 768GB ECC DDR5; two NVIDIA RTX PRO 6000 96GB GPUs; 192GB aggregate VRAM and high-capacity NVMe scratch.
The current compute node is based on the Supermicro AS-4125GS-TNRT / CSE-418G2TS 4U rack platform, integrated with matched rails, power, management, networking and rack infrastructure as required.
Best-fit workloads
Two-GPU AI training, genomics/protein or microscopy models, robotics perception, synthetic-data generation, visualisation and accelerated scientific workflows.
It is the practical step from single-GPU development to data-parallel or model-parallel training without introducing a cluster fabric.
Recommended software stack
CUDA, NCCL, PyTorch Distributed, TensorFlow or JAX, RAPIDS, MONAI, DeepStream, Apptainer/Docker, MLflow and Nsight Systems.
Pin host drivers and infrastructure separately from versioned application containers. Record source, image, dataset/model and hardware allocation with each benchmark or production release.
Deployment pattern
Use NCCL-enabled framework containers and keep active datasets/checkpoints on local NVMe. Separate training from production inference and preserve dataset/model lineage in a governed experiment store.
Define monitoring, identity, backup, change control and workload ownership at the same time as compute. Multi-user platforms require resource allocation and quotas; production services require health, overload and rollback behaviour.
Sizing boundary
Two GPUs require software that is explicitly configured for distributed execution. Validate scaling efficiency and storage throughput before assuming a 2x speed-up.
Final sizing should use representative code, data, concurrency and service objectives. Aggregate core, RAM or VRAM figures do not by themselves predict application performance.
Commissioning and acceptance
Establish one-GPU correctness/performance, enable two-GPU execution, measure scaling and communication, test checkpoint restart and verify storage can sustain the input pipeline.
Axiotech should retain the resulting configuration, firmware/driver baseline, environment manifest, benchmark data and recovery procedure as the system acceptance pack.
Primary technical references
References are provided for software architecture and implementation planning. Validate the versions, licences, support matrix and regulated-use requirements applicable to the final deployment.