More small companies, colleges and startups now want AI that runs on their own machines: a private assistant for staff, an AI coding helper, or a system that answers questions from company documents - without sending confidential files to an outside service. This guide explains what hardware you actually need, in plain language.
1. The one number that matters most: VRAM
AI language models are loaded into the memory of the graphics card, called VRAM. If the model does not fit, it either will not run or becomes very slow. So the first question is always: which models do you want to run? Model size is measured in parameters (for example 8B, 32B or 70B - billions of parameters).
- 16 GB VRAM - comfortably runs models up to about 14B. Good for one person: a private assistant, coding help, document chat.
- 24-32 GB VRAM (RTX 4090 or RTX 5090) - runs models up to about 32B, plus image generation and small fine-tuning.
- About 96 GB VRAM (NVIDIA RTX PRO 6000 Blackwell) - runs 70B-class models on one card, and handles training and research work.
These are rough guides. The exact figure depends on how the model is compressed and how much text it needs to remember in one conversation.
2. RTX 5090 or RTX PRO 6000?
Both graphics cards have very fast memory, so for models that fit on both, their speed is similar. The deciding question is simple: does your biggest model fit in 32 GB? If yes, the RTX 5090 is the better value. If you need 70B-class models, training, or the most memory on one card, the RTX PRO 6000 with 96 GB is the right choice - but plan for about 600 watts of power and good cooling.
3. The compact option: DGX Spark, Ryzen AI Max and Mac Studio
A newer kind of AI computer uses large shared memory instead of a separate graphics card. NVIDIA DGX Spark and AMD Ryzen AI Max+ 395 systems offer 128 GB in a small, quiet box, so they can load very large models. The trade-off: their memory is slower than a high-end graphics card, so they generate answers more slowly. They suit one or two people experimenting with big models. For a team that uses AI all day, a GPU workstation or server is faster.
4. One workstation or a team server?
A workstation sits at one desk and serves one or two people. A team AI server sits in your server room, connects to your office network and gives everyone a private ChatGPT-style web page with their own login. If more than a few people will use AI daily, a server with one or more graphics cards is usually the better investment.
5. Do not forget power, heat and noise
High-end AI graphics cards draw a lot of power and produce heat. Running all day, they need a quality power supply, a suitable UPS, good airflow and sometimes a separate room. These are often forgotten in quotes - make sure yours includes them.
6. Own hardware or cloud AI?
For a team using AI every day, your own hardware often pays for itself over one to two years and keeps your data private. For light or occasional use, a cloud subscription can be cheaper. The honest answer depends on how many people will use it and how often.
7. The software matters as much as the hardware
A powerful machine is only useful once the AI software is set up: drivers, an AI runtime such as Ollama or vLLM, a web interface such as Open WebUI, and - if you want answers from your own documents - a private document search system. Ask your builder to include setup and training, not just the box.
Need help choosing?
Sun G Computers builds AI workstations and AI servers in Vijayawada and delivers across India. Tell us what you want AI to do for your team and we will recommend the right tier - including whether you need it at all. Call or WhatsApp +91 92814 65676 or see our AI workstation and server page.