A large-memory single-GPU platform for private LLM inference, retrieval-augmented generation, medical imaging and large scientific 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; one NVIDIA RTX PRO 6000 96GB GPU; enterprise 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
Large private LLM inference, retrieval-augmented generation, large medical-imaging models, high-resolution scientific inference and any workload whose simplest safe fit is one 96GB accelerator.
Choose it when model fit, a simple single-GPU execution model and 96GB of directly addressable VRAM matter more than replica density.
Recommended software stack
CUDA, PyTorch, vLLM or TensorRT-LLM, Triton, Hugging Face tooling, vector databases, MONAI, MLflow and Prometheus.
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
Prefer a single model process or controlled server such as vLLM/Triton, backed by 768GB host memory for retrieval, preprocessing and cache. Add an authenticated gateway and protected model repository.
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
One GPU limits replica-level concurrency. If throughput is the priority and the model fits 24GB, X1440 can be more efficient; if training needs multiple 96GB GPUs, use X1260 or X1460.
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
Confirm the maximum context/batch fits with headroom, test cold start and sustained concurrency, verify rollback, and record time-to-first-result, throughput, VRAM and host-memory high-water mark.
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.