A CPU- and memory-led HPC platform for simulation, genomics, analytics, compilation and workloads with large in-memory working sets.
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; 128 AMD EPYC cores; 1.15TB ECC DDR5; one NVIDIA RTX PRO 4000 24GB; enterprise U.2 NVMe.
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
Genome assembly, CFD/FEA preprocessing and single-node solves, mathematical computing, large compilation/test farms, digital-twin simulation and any workload dominated by CPU cores or in-memory state.
Choose it when CPU cores, memory capacity and memory bandwidth dominate, with a professional GPU available for visualisation or selective acceleration.
Recommended software stack
Linux, GCC/LLVM/AOCC, OpenMPI, OpenMP, Python/NumPy/SciPy, R, Julia, MATLAB, PETSc, Nextflow, Apptainer, Slurm client tooling and profiling utilities.
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 NUMA-aware processes/threads, versioned modules or containers and local NVMe for temporary work. It can operate alone or as a login/development baseline for a later scheduled CPU cluster.
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
Highly GPU-bound workloads will underuse the host platform. Measure arithmetic intensity and accelerator utilisation before selecting a CPU-led design.
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
Measure serial and parallel efficiency, NUMA locality, peak RAM, memory bandwidth sensitivity and NVMe throughput; verify that the 24GB GPU path is useful rather than assumed.
Axiotech should retain the resulting configuration, firmware/driver baseline, environment manifest, benchmark data and recovery procedure as the system acceptance pack.
Primary technical references
- NVIDIA Container Toolkit overview
- Prometheus overview
- Slurm Quick Start Administrator Guide
- NVIDIA CUDA C++ Best Practices Guide
References are provided for software architecture and implementation planning. Validate the versions, licences, support matrix and regulated-use requirements applicable to the final deployment.