VijayCloud https://validator.w3.org/feed/docs/rss2.html Home Video Studio Product Ad Video Logging AI workloads without creating a liability What belongs in a data processing agreement with a GPU provider The specs that throttle training and are not the GPU Reading a GPU spec sheet: which numbers actually matter Submit Your Brief AI Spokesperson Video Social Reel (9:16) Logo Animation Cinematic B-Roll Explainer Video What to monitor on a GPU box Quantisation for serving: what you gain, what you give up Serving agents is not serving chat Why fine-tunes fail in production, and it is rarely the model Data residency and data sovereignty are not the same thing The utilisation problem: why most GPU spend is wasted Serving concurrency: how the KV cache sets your user ceiling LoRA, QLoRA or a full fine-tune: how to choose Where your prompts actually go, and what self-hosting does not fix Features Contact What a fine-tune actually costs when the GPU is billed by the month Seven questions to ask a GPU provider before you commit Sizing VRAM: how batch size and context length decide whether your job runs Keeping training data in India: what the DPDP Act actually asks of you What “dedicated GPU” actually means, and how to check RTX 16 GB or A100 40 GB: which one does your fine-tune actually need? Blog Refund Policy Pricing About Us Careers Privacy Policy Terms of Service Legal Medusa — Private AI Assistant Enterprise — multi-GPU and bare metal Professional — 1× NVIDIA A100 (40 GB) Starter — 1× NVIDIA RTX (16 GB) Shop