Take a two-person AI shop with a $2,000 hardware budget and a model it wants to serve.
That budget used to cover a working rig.
This month it buys a place in a queue.
The Tesla V100, a workstation card from 2017, moves in truckloads for inference work and now costs $200 more than it did before the AI boom pulled buyers toward it.
The 32GB of HBM2 and 900GB/s of bandwidth explain why.
The RTX 6000 PRO crossed $16,000 in August, and the RTX 5090 is no longer listed in NVIDIA's marketplace.
The used market is where the shift shows up.
Four V100s on an NVLink board beat an M5 Ultra and dual DGX Spark units by up to 230% on the Qwen3.8-Flash-Next benchmark.
Buyers are paying a premium for nine-year-old silicon because it still does the job at a fraction of the price of new hardware.
The decision then stops being which card to buy and becomes which card to rent.
On the rented side of that decision, RTX 3090 compute at $0.15/hr runs the experiments and cloud storage at $2.49/TB per month keeps the datasets beside it.
Buy Now Pay Later means zero upfront and payment one month after you start.
What is the one experiment you keep postponing because the hardware quote is too high?
https://ai.bhkcloud.com/?utm_source=linkedin&utm_medium=social&utm_campaign=bhk_social_2026w42&utm_content=morning
Originally posted on linkedin_personal