VijayCloud https://validator.w3.org/feed/docs/rss2.html Privacy Policy About Us Pricing Blog Why your first request takes minutes and the second takes milliseconds The line items people miss on a GPU bill Evaluating a fine-tune before you ship it Fitting a bigger training run on the GPU you already have When one GPU stops being enough Building a dataset worth the GPU hours Choosing a serving stack Batching and streaming without hurting latency RTX 16 GB or A100 40 GB: which one does your fine-tune actually need? What “dedicated GPU” actually means, and how to check Keeping training data in India: what the DPDP Act actually asks of you Reading a GPU spec sheet: which numbers actually matter The specs that throttle training and are not the GPU What belongs in a data processing agreement with a GPU provider Logging AI workloads without creating a liability Why fine-tunes fail in production, and it is rarely the model Serving agents is not serving chat The utilisation problem: why most GPU spend is wasted What a fine-tune actually costs when the GPU is billed by the month Sizing VRAM: how batch size and context length decide whether your job runs Where your prompts actually go, and what self-hosting does not fix Seven questions to ask a GPU provider before you commit Data residency and data sovereignty are not the same thing LoRA, QLoRA or a full fine-tune: how to choose Quantisation for serving: what you gain, what you give up Serving concurrency: how the KV cache sets your user ceiling What to monitor on a GPU box Home Contact Video Studio Product Ad Video Submit Your Brief Features AI Spokesperson Video Social Reel (9:16) Logo Animation Cinematic B-Roll Explainer Video Refund Policy Careers 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)