A NVIDIA AI chip led the pack in a leading performance test for large language models (LLMs) with an Intel semiconductor ranking a close second.

MLCommons’s MLPerf Inference benchmarking suite measures how fast systems can run LLMs in different scenarios. MLCommons is a collaborative engineering non-profit organisation focused on developing the AI ecosystem through benchmarks, public datasets and research. Members include startups, large businesses, academics, and non-profits.

According to MLCommons, the organisation’s benchmarking tools will prove effective for companies purchasing, configuring, and optimising machine learning applications, as well as for designing next-generation systems and technologies.

Nvidia has seen its fortunes rising with the development of AI which relies on the advanced semiconductors that the company produces. On 8 September, the company announced a new open-source software suite called TensorRT-LLM for LLM optimisation using Nvidia graphics processing units (GPUs) to improve AI inference performance after deployment. AI inference is the process a LLM usea to deal with new data when it needs to summarise, produce code or answer queries.

According to research analyst GlobalData, Nvidia is set to benefit from the positive outlook for the global AI market which the analyst forecasts to reach $241bn by 2025.

In terms of corporate strategy, Nvidia intends to expand its AI technology and platform offerings to become globally competitive within the AI market, according to GlobalData.

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In March 2023, the company collaborated with Google Cloud to offer a new generative AI platform. Nvidia’s inference platform for generative AI will be integrated into Google Cloud Vertex AI to accelerate the work of companies establishing a rapidly expanding number of generative AI applications, according to GlobalData.