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AI development and scientific computing

Running and training models is decided by graphics-card memory first: several cards at full PCIe speed let larger models fit. Scientific and numerical work scales with core count and memory bandwidth, and model size is limited by memory. System memory should be at least the size of the total video memory.

Typical software

  • PyTorch Training
  • Hugging Face Models
  • llama.cpp and Ollama Local LLMs
  • ComfyUI Image generation
  • MATLAB Analysis
  • Python data science Analysis
  • Ansys Simulation
  • Virtualization Proxmox, VMware

What matters

Graphics
Critical
GPU memory decides which models fit, and several GPUs let you run larger ones. Only some solvers use the GPU, so we check yours first.
Memory
Critical
System memory of at least your total GPU memory, ideally twice that. Large meshes may need 256 GB or more.
Processor
Critical
Solvers scale with core count and memory bandwidth, where Threadripper PRO helps. For AI work it mainly feeds the GPUs, so PCIe lanes matter more than raw speed.
Storage
Important
Large NVMe drives, since models and datasets fill space quickly, plus fast scratch space for solver files.