RTX PRO buyers weigh 32GB, 48GB, 96GB

This is less about raw model size than about concurrency, context, and operational headroom. 32GB can work for a single quantized service, but once you add multiple users, larger KV caches, or LoRA/QLoRA experiments, the friction shows up fast.

• –32GB is viable for proof-of-concept inference and smaller multimodal pipelines, but it leaves little margin once you run retrieval, vision, and structured extraction together
• –48GB is the practical middle ground for an on-prem pilot: enough room for stronger 30B-class quantized models, larger contexts, and some concurrent serving
• –96GB is the “buy once, cry once” option if you want to avoid a near-term refresh and keep finetuning or larger reasoning models in play
• –For 1.5TB of RAG data, VRAM is not the main storage bottleneck; CPU RAM, indexing, and IO architecture matter at least as much
• –If the workload is genuinely multi-user and growth-minded, 48GB is the floor I’d trust, while 96GB is the safer long-term bet