What it means
An ordinary computer processor, the CPU, is very good at doing a few complicated tasks one after another. A GPU does the opposite, using thousands of small processing units to do many simple calculations in parallel.
This design suits tasks such as rendering graphics, simulating physics and the matrix maths that sits behind modern AI. The business impact is big.
Companies that train AI models, run large simulations or process video need GPUs, and demand has pushed up prices and created waiting lists. Cloud providers rent GPU time by the hour, while some firms buy the hardware and run it in their own data centres.
For a finance team, a GPU decision is a classic buy-or-rent question. Buying means a large upfront payment recorded as a fixed asset and depreciated, which is spread as a cost over its useful life, plus electricity and cooling.
Renting converts that into an operating cost that rises and falls with use, with no capital tied up. Useful life is a contested point.
GPUs improve quickly, so an expensive chip can look outdated in a few years, and a depreciation period that is too long may overstate profit. Many companies choose a period of three to six years, and analysts keep a close eye on whether this is realistic.
GPUs also matter to investors. Chip makers, cloud providers and data centre owners all earn income from the demand, and a company's spending on GPUs is often read as a signal of how seriously it is investing in AI.
The same spending, however, can weigh on cash flow if the expected returns are slow to arrive.
In practice
Real-world examples.
Example
A video streaming company uses a cluster of GPUs to encode thousands of hours of new content each week. Because the machines run nearly all day, the finance team finds that owning the hardware is cheaper than renting from a cloud provider.
Example
A start-up building a language tool needs GPUs for only six weeks each year to train its model. It rents cloud capacity for those weeks, which avoids a $300,000 purchase and keeps its balance sheet light.
Example
A bank's risk team runs overnight simulations of thousands of market scenarios. By moving the calculations to GPUs, the job drops from nine hours to under one, and the saving in staff time and faster decisions pays for the hardware.
Formula
Calculation
Break-even utilisation = Total cost of owning / Total cost of renting at full use
Suppose a company needs 8 GPUs for three years. Buying costs 8 x $25,000 = $200,000 plus $20,000 a year for power and hosting, so total ownership cost over three years is 200,000 + (3 x 20,000) = $260,000. Renting costs $2.50 per GPU per hour, which for 8 GPUs running every hour of a year is 8 x 2.50 x 8,760 = $175,200, or 3 x 175,200 = $525,600 over three years. Break-even utilisation = 260,000 / 525,600 = 0.495, or about 49.5%. If the GPUs would be busy more than about half the time, buying is cheaper; below that, renting wins.Case study
Seen in the real world.
Northgate Analytics is an illustrative, fictional data company that planned to launch an AI product. The chief financial officer received two proposals: buy 16 GPUs for $400,000, or rent equivalent capacity at a cost of about $350,000 a year.
She asked how busy the machines would be. Forecast use was 30% in year one, rising to 60% by year three, so rental cost in year one would be only about $105,000 while owned machines would sit idle most of the time.
She chose to rent for the first year, collect real usage data and then buy once utilisation passed 50%. The fictional company avoided tying up $400,000 early, and the board noted that the purchase decision was now based on evidence instead of enthusiasm.
Watch out
Common mistakes.
- Comparing only the purchase price with the rental price, and ignoring power, cooling, staff and the time the chips sit idle.
- Depreciating GPUs over a long life, when rapid improvements can make them obsolete sooner than planned.
- Assuming all GPU spending leads to revenue, when many projects use large amounts of computing before they earn anything.
Questions
People also ask.
Is a GPU only used for graphics?
No, although the name comes from graphics, the same design is now used for AI, scientific research and financial modelling.
How are GPUs shown in the accounts?
Purchased GPUs are normally recorded as property, plant and equipment and depreciated, while rented capacity is shown as an operating expense.
Why are GPU prices so volatile?
Demand from AI projects can exceed supply for long periods, and prices rise and fall with that balance and with the arrival of newer chips.
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