A professional development and proof-of-concept node for CUDA, AI, data science and software engineering.
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; 384GB ECC DDR5; one NVIDIA RTX PRO 4000 24GB; enterprise NVMe and remote management.
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
CUDA and AI development, confidential proofs of concept, GPU-enabled CI, data-science workbenches, teaching laboratories and modest inference services.
Choose it when one professional GPU is enough for development, inference experiments and modest training, while CPU capacity and ECC memory must remain server-class.
Recommended software stack
Ubuntu or enterprise Linux, CUDA, PyTorch, TensorFlow or JAX, NVIDIA Container Toolkit, Docker/Podman, GitLab Runner or GitHub Actions runner, 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 versioned containers for framework projects, a protected registry and separate project workspaces. It can be a dedicated team server or a development tier whose tested images promote to larger Hyperion systems.
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
The 24GB accelerator is not intended for monolithic very-large-model training. Use quantisation, parameter-efficient fine-tuning or graduate to X1160/X1260 when the working set exceeds local VRAM.
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
Compile and run a CUDA sample, train or infer a representative model, verify ECC/remote management, benchmark NVMe and record GPU utilisation, peak VRAM and end-to-end runtime.
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.