Hey everyone,
I’m looking to build or buy a PC tailored specifically for training AI models for Computer Vision and Signal Classification that will eventually be deployed on edge hardware like the Hailo-8, NVIDIA Jetson, or similar accelerators. My goal is to create an efficient setup that balances cost and performance while ensuring smooth training and compatibility with these devices.
Details About My Needs
- Model Training: I’ll be training deep learning models (e.g., CNNs, RNNs) using frameworks like TensorFlow, PyTorch, HuggingFace, and ONNX.
- Edge Device Constraints: The edge devices I’m targeting have limited resources, so my workflow might includes model optimization techniques like quantization and pruning.
- Inference Testing: I plan to experiment with real-time inference tests on Hailo-8 or Jetson hardware during the development phase.
- Use Case: My primary application involves object detection (for work) and, at a later stage, signal classification. For both cases, recall is our highest priority (missed true positives are fatal). Precision is also important (We don’t, want false alarms, but better having some false alarms then missing an event)
Questions for Recommendations
- CPU: What’s the ideal number of cores, and which models would be most suitable?
- GPU: Suggestions for GPUs with sufficient VRAM and CUDA support for training large models?
- RAM: How much memory is optimal for this type of work?
- Storage: What NVMe SSD sizes and additional HDD/SSD options would you recommend for data storage?
- Motherboard & Other Components: Any advice on compatibility with Hailo-8 or considerations for future upgrades?
- Additional Tips: Any recommendations for OS, cooling, or other peripherals that might improve efficiency?
If you’ve worked on similar projects or have experience training models for deployment on these devices, I’d love to hear your thoughts and recommendations!
Thanks in advance for your help!