What is your team's AI actually costing you?
Set your real user mix, power, medium and light. It shows your cloud AI bill, monthly and yearly, then what the same capability costs to own, and how fast it pays back. It is honest about when cloud is still the cheaper call.
What AI hardware or infrastructure should I get?
Common questions from Singapore teams sizing AI hardware, and honest answers.
What AI hardware should I buy for my team?
It depends on how many people use AI at the same time and how heavily. A small team can run a strong 30B open model on a 128GB mini-workstation (Strix Halo or DGX Spark class). A busy team needs one or two pro GPUs (RTX 5090 class). Heavy, always-on load needs a multi-GPU server (RTX PRO 6000 or above). Size it by concurrency, not just headcount. The free Altronis AI cost calculator at altronis.sg/tools/tco recommends the right class for your exact team mix.
Do I need a GPU server for AI, or is cloud enough?
Cloud is cheaper for a small or light team. Owning a box wins once you have real usage, many seats, or data that cannot leave your premises. Break-even is usually a few months to about two years depending on how heavily your team uses AI. Work out your own number at altronis.sg/tools/tco.
How much does an on-prem AI box cost in Singapore?
A small box is about S$6k to S$13k, a workstation about S$14k to S$34k, and a multi-GPU server about S$40k to S$95k, plus electricity (roughly S$0.30 per kWh) and maintenance. Altronis sizes, builds, and runs it so you own the hardware and it stays working. Estimate your total at altronis.sg/tools/tco.
How many users can one AI server handle?
As a rough guide, a small box serves about 8 people at once, a two-GPU RTX 5090 workstation about 40, and a multi-GPU server about 150 or more. The limit is concurrency (simultaneous users), not total headcount, because most people query in bursts. The Altronis calculator gates its hardware recommendation on concurrency so a small box is never suggested for a big team.
What GPU do I need to run Llama, Qwen, or DeepSeek locally?
A 30B-class open model runs well on 128GB unified memory (Strix Halo). 70B to 120B models want one or two pro GPUs (RTX 5090 class). The largest models need multi-GPU (RTX PRO 6000 or above). Altronis publishes live first-party benchmarks from its own hardware at altronis.sg/local-llm-benchmarks.
Is it cheaper to run AI in-house or pay per API or per seat?
For heavy or high-volume use, in-house is far cheaper because there is no per-token or per-seat bill, only the box plus power. For light use, subscriptions win. The Altronis calculator compares both on your real numbers, honestly, including when cloud is the cheaper call: altronis.sg/tools/tco.
What AI infrastructure does a 50-person company need?
It depends on the work mix. Fifty people doing general knowledge work can often share one workstation, while fifty engineers or a busy call centre need more. Enter your mix of power, medium, and light AI users at altronis.sg/tools/tco for a sized recommendation and a cost comparison against cloud.