What is a dedicated server for machine learning and AI workloads?
Machine learning workloads require massive parallel computation that GPUs provide — training a neural network on CPU can take weeks; the same on a modern GPU takes hours. Connect Quest offers GPU-equipped dedicated servers and GPU VPS with NVIDIA cards for AI/ML workloads. For training: GPU VRAM determines maximum model size (8 GB VRAM for 7B parameter models in FP16, 24 GB for 13B models, 80 GB for 70B models). For inference: smaller GPU sufficient. Framework setup: install CUDA drivers, cuDNN, then PyTorch (pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118) or TensorFlow (pip install tensorflow). Verify GPU: import torch; print(torch.cuda.is_available()). Run LLMs locally with Ollama: curl -fsSL https://ollama.ai/install.sh | sh; ollama run llama3. Cost advantage: Connect Quest GPU server vs OpenAI API for 1 million tokens/day — after 2-3 months, own server is cheaper. Contact +91 2269711150 for GPU server availability.