To rent a cloud GPU: (1) pick a provider like RunPod or Vast.ai, (2) choose a GPU that fits your model’s VRAM needs, (3) launch an instance on-demand or spot, (4) connect via SSH, a Jupyter notebook, or a container template, and (5) stop the instance when you’re done to avoid charges. Most providers bill … Read more
A cloud GPU is a graphics processing unit you rent remotely over the internet instead of buying physical hardware. You pay by the hour — or even by the second — to run AI training, inference, rendering, or HPC workloads on powerful data-center GPUs like the NVIDIA H100, without the upfront cost of owning one. … Read more
A 70-billion-parameter LLM needs about 140GB of VRAM for FP16 inference (2 bytes per parameter), or roughly 35–40GB when quantized to 4-bit. Training needs 1.5–4x more than inference for optimizer states and gradients. As a quick rule, budget ~2GB of VRAM per 1B parameters at FP16 for inference — then add overhead for the KV … Read more
