Frequently asked questions about GPU dedicated servers for AI & machine learning
If you are planning to deploy AI, machine learning, or deep learning workloads on GPU dedicated servers, these are some of the questions we hear most often.
What GPU models are available on Primcast dedicated servers?
We offer enterprise-grade NVIDIA A100 and H100 GPUs with high-bandwidth memory (HBM), optimized for training deep learning models and running AI inference workloads at scale.
Are the GPU servers pre-configured with CUDA and ML frameworks?
Yes, our GPU dedicated servers come with pre-installed CUDA drivers and support for popular frameworks like TensorFlow, PyTorch, Keras, and Caffe. You can start training your models immediately after deployment.
Can I scale my GPU resources as my ML workloads grow?
Absolutely. You can upgrade to higher-tier GPU configurations or add additional servers as your training datasets and model complexity increase. Our team can help design a scalable infrastructure for your AI projects.
What kind of network performance can I expect for large dataset transfers?
Our GPU dedicated servers are connected to a low-latency global network with GPU-optimized bandwidth, enabling fast transfers of large training datasets, model checkpoints, and inference results.
Do you provide support for GPU-specific issues and optimization?
Yes, our support team includes GPU specialists who can assist with CUDA optimization, memory management, multi-GPU training setup, and troubleshooting GPU-specific performance issues 24/7.
