01
AI, LLMs & private enterprise AI
Plan training, fine-tuning, inference, retrieval, and private AI around your model and service requirements. The same GPU count can produce very different results across applications.
- Model size, precision, context window, and KV-cache planning
- Throughput, latency, concurrency, and representative evaluation workloads
- Data locality, access control requirements, and software entitlements
02
HPC, simulation & scientific research
Engineering solvers and research applications need the right precision, memory bandwidth, and interconnect. Compare supported platforms using your actual code and dataset.
- FP64 versus FP32 or mixed-precision requirements
- Solver licensing and GPU acceleration support
- Cluster scheduling, shared storage, and measured scaling
03
Rendering, visualization & analytics
Scope systems for 3D rendering, digital twins, engineering visualization, and GPU-accelerated data analytics.
- Scene and dataset memory requirements
- GPU rendering engine, driver, and application compatibility
- Data ingestion, local NVMe scratch, and concurrent users
04
Cloud GPU & data-center infrastructure
Build a deployment plan for GPU service offerings, enterprise capacity, or a research cluster. Think through provisioning and facility readiness before the first rack arrives.
- Compute, storage, management, and customer network separation
- Tenant isolation and supported virtualization or partitioning
- Pilot acceptance, phased delivery, spares, and repeat procurement