In an attempt to challenge NVIDIA Corp. NVDAGiven Asia’s dominance in the field of artificial intelligence, Asian start-ups are developing more energy-efficient and cost-effective chips for specific artificial intelligence applications.

What happened: Asian startups are challenging Nvidia’s dominance in the AI ​​space by developing more energy-efficient and cost-effective chips for specific AI applications. These startups are targeting the gap in the market left by Nvidia’s high power consumption and clunky design, Nikkei Asia reported on Friday.

These startups focus on two types of AI chips: “inference” chips, used to run existing AI models, and “training” chips, powerful data processing components used to develop new AI models.

While Nvidia’s GPUs continue to dominate the AI ​​landscape, startups believe that the high power consumption and clunky design of their GPUs leave a gap in the market that they can fill.

These startups believe that while Nvidia’s GPUs are powerful, they are too power-intensive and expensive for many applications. Preferred networks (PFN) Managing Director Nishikawa Toru stated: “Nobody has developed the perfect chip architecture for inference.” PFN is developing chips that are intended to be more efficient and more cost-effective than Nvidia’s offerings.

Nvidia’s GPUs are primarily used to train AI models, but their high cost and high power consumption make them impractical for devices such as laptops and wearables. Analysts, including Kazuhiro Sugiyama out of Omdiabelieve that the demand for device-embedded AI will increase and encourage new entrants to enter the market.

Startups like Edgecortixled by Sakyasingha Dasguptaare focusing on solving problems such as the “memory wall” problem to develop slimmer and more energy-efficient AI chips. These efforts are part of a broader strategy to meet the growing demand for AI in industrial applications and robotics, especially in Asia, the report said.

“Nvidia’s GPU is mainly for training, but we are seeing more and more new entrants developing chips aimed at both training and inference,” Sugiyama said.

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Other companies entering the market include SambaNova systemssupported by SoftBank Group‘S SFTBY SFTBF Vision Fund; Tenstorrentfounded by a former Intel Corp INTC engineer; and the British company Graphcorewhich was recently acquired by SoftBank.

Large technology companies such as Alphabet Inc.‘S GOOG GOOG Google, Meta Platforms Inc. METAAnd Amazon.com Inc.S Amazon Amazon Web Services are also there, along with Nvidia’s rival For more information, visit: Advanced Micro Devices, Inc. AMD.

Why it is important: Competition between Nvidia and emerging Asian startups is heating up as the market for AI chips continues to grow. Eric Schmidtformer CEO of Googlehighlighted Nvidia as a major player in the AI ​​sector, noting that major technology companies are planning significant investments in Nvidia-based AI data centers that could potentially cost up to $300 billion.

Meanwhile, SoftBank has suffered setbacks in its bid to compete with Nvidia with its own AI chip production. Negotiations with Intel reportedly failed due to Intel’s inability to meet production requirements, prompting SoftBank to turn to Taiwan Semiconductor Manufacturing Co. TSMa major supplier to Nvidia.

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This story was created with Benzinga Neuro and edited by Kaustubh Bagalkote