"You can take pretty much any large language model you want and put it in this and it will inference like crazy," Huang said. But unlike training, inference takes place near-constantly, while training is only required when the model needs updating. Like training, inference is computationally expensive, and it requires a lot of processing power every time the software runs, like when it works to generate a text or image. Then the model is used in software to make predictions or generate content, using a process called inference. Oftentimes, the process of working with AI models is split into at least two parts: training and inference.įirst, a model is trained using large amounts of data, a process that can take months and sometimes requires thousands of GPUs, such as, in Nvidia's case, its H100 and A100 chips. Nvidia representatives declined to give a price. The new chip will be available from Nvidia's distributors in the second quarter of next year, Huang said, and should be available for sampling by the end of the year. He added, "This processor is designed for the scale-out of the world's data centers." "We're giving this processor a boost," Nvidia CEO Jensen Huang said in a talk at a conference on Tuesday. But the GH200 pairs that GPU with 141 gigabytes of cutting-edge memory, as well as a 72-core ARM central processor. Nvidia's new chip, the GH200, has the same GPU as the company's current highest-end AI chip, the H100. Personal Loans for 670 Credit Score or Lower Personal Loans for 580 Credit Score or Lower Best Debt Consolidation Loans for Bad Credit
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