Ask what – if anything – is catching the AI industry back, and the answer you receive depends on whom you are talking to. I asked one Bloomberg Former main data Vranalers, Carmen LeeAnd his answer was “value transparency”.
According to LI, most of the small AI companies’ inability to estimate how much they would need to spend for the privilege of renting time on GPU to train their models, made their businesses unpredictable and AI has unnecessarily expensive. He set up a startup Silicon data To create a solution: First world-wide fare price index for a GPU.
The price index of that rental, called Sdh100rtLaunched today. Every day, it will crunch 3.5 million data points from more than 30 sources around the world, so that the average spot rent can be given to use one. Nvidia h100 GPU for one hour. (“Spot Price” is the one that is delivered a commodity immediately.)
“I really believe that the calculation will be the greatest resource for humanity over the next few years,” Li says. “If my thesis is correct, it will require more sophisticated risk management.”
According to Lee, such an index will have more opportunities for a broad set of players to join the AI Tool and AI industry. How do you get from an index? The basic story of silicone data helps explain it.
US $ 1.04: Nvidia H100 GPU on the Eastern Coast of the United States benefits the price of rent for those on the West Coast.
By the beginning of last year, Lee was in charge of global data integration BloombergIn that case she met with several small companies that were trying to distribute AI-fuel data products, and many of them were struggling with the same problem. They could only offer their products at a certain rate, but the cost of the GPU-time was unpredictable to them. Therefore, so their benefits were margins.
With specific items such as energy, companies can plan for these swings by knowing historical trends and hedging with financial products such as futures contracts. But it was not present for the main object of AI, time on a GPU. Hence Lee has been determined to build the foundation for those products, and the result is the SDH100RT price index.
He chose to seize the Nvidia H100, as it is the most widely deployed GPU, and is used to train the new AI model. However, a price index for Nvidia A100s, which deal with a lot of estimate tasks, works as well. And he has developed a method that will determine when it makes sense to the indexed prices for other AI chips, such as AMD and the Blackwell series of NVidia.
Carmen Lee established silicone data after a term in Bloomberg.Silicon data
The creation of new AI products will be able to better understand their potential costs armed with data, startups and others, so they can set their services to a profitable price. And the new AI infrastructure will be able to set a benchmark for their own revenue. But just as, in Lee’s opinion, it is that the new sources of capital AI may join the space.
For example, Bank Capital, is a relatively cheap supplier of notes Lee. But because they have strict risk control and do not have enough GPU price data, they are not in a position to fund AI projects. Lee hopes that SDH100RT will allow banks to lend to a broad set of players in the AI industry and allow them to come up with financial products that already reduce the risk for those.
Insight and inequalities from data
Although it has been launched today, silicone data has monitored the GPU fare prices for months. As you can expect, some interesting insights have been unveiled by having a window in the price of AI training. There are some things that Lee has discovered. (She is publishing This analysis Regularly since last September.)
Eastern coast rules! West Coast Drols: H100 rental price is very stable in the United States, but there is one Constant east coast profitIn March, you can find one hour work from H100 to US $ 5.76 on the East Coast. But the price of the same hour will be $ 6.80 on the West Coast.
Help Hyperscaler Chips: Amazon Web Service Forest in Design your own chips And the server is Low prices For cloud giant customers. According to silicon data, the price of around $ 4.80 per GPU per GPU per GPU 2 of AWS is lower than half of the price to use an Nvidia H100. Its first generation chips infantia and trainium come in less than $ 1.50 per hour, which is less than half of the price of today’s estimates, NVDia A100. However, H100s is considered the only option for state -of -the -art model training, so their performance can justify extra scratches.
Deepsek’s minor effects: January lampsake shock Very little Spot for the price of rent. You may recall that the performance and Hangzo-based lamp’s LLM of LLM reported and many surprised and sent AI-related stocks to a patch of Pearl Clutching. “When Deepsek came out, the (stock) market went crazy,” says Lee. “But the price of the spot did not change much.” At the beginning of the lampsac, the H100 price rose lightly to $ 2.50 per hour, but it was still from months before the range of $ 2.60 per hour in $ 2.40 per hour. It then slipped to $ 2.30 per hour for most of February, before it started climbing again.
Intel is more posh than AMD: The GPUs are always under the control of the CPU, usually in 4: 1 ratio. And for that CPU spot, elections have been contested between the market Intel and AMD. (Nvidia also makes its CPU, called Grace.) But it seems that customers are willing to pay a little premium for the Intel-Integrated System. For the NVIDIA A100 system, people with Intel CPUS receive about 40 percent higher price than people with AMD processor. For H100, the effect involves the interconnect technique. If a computer used SXM or PCIE as its link, Intel received a high price. But for those using NVLINK interconnect scheme of Nvidia, AMD received a premium.
Commodity of AI
Can you really boil the price of AI at the same number? After all, the performance of the computer and its utility for a particular customer include a lot of factors. For example, a customer can undergo training with data, which, for legal reasons, cannot cross international borders. So why should they care about the price in another country? And, as anyone has examined the major benchmark results of machine learning, Mlperf, can see, the performance of the same NVidia GPU may vary widely depending on the system that is in it and it is running software.
According to Lee, the commodity view can work. The index of silicone data normalizes all these differences and gives them different weight such as a data center participates in the rental market, its location, its data sources and many other things.
Perhaps the greatest support for AI’s idea as an object Nvidia CEO Jensen Huang she herself. In the company’s big developer event, GTCHe pushed the data centers to think as “AI factories”, whose output would be measured on how many tokens, the smallest unit of information using LLM, can produce per second.
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