In a bid to challenge $NVIDIA (NVDA.US)$'s AI dominance, Asian startups are developing more energy-efficient and cost-effective chips for specific artificial intelligence applications.
What Happened: Asian startups are challenging Nvidia's AI dominance 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 energy consumption and bulky design, reported Nikkei Asia on Friday.
These startups are focusing on two types of AI chips: "inference" chips, used to operate existing AI models, and "training" chips, high-powered data-processing components used to develop new AI models.
While Nvidia's GPUs continue to dominate the AI landscape, the startups believe that their GPUs' high energy consumption and bulky design leave a gap in the market that they can fill.
These startups believe that Nvidia's GPUs, while powerful, are too energy-intensive and expensive for many applications. Preferred Networks (PFN) CEO Toru Nishikawa stated, "No one has come up with the perfect chip architecture for inference." PFN is developing chips that aim to be more efficient and less costly than Nvidia's offerings.
Nvidia's GPUs are primarily used for training AI models, but their high cost and energy consumption make them impractical for devices like laptops and wearables. Analysts, including Kazuhiro Sugiyama from Omdia, believe that the demand for on-device AI will rise, encouraging new entrants to the market.
Startups such as Edgecortix, led by Sakyasingha Dasgupta, are focusing on solving issues like the "memory wall" problem to create more streamlined and energy-efficient AI chips. These efforts are part of a broader strategy to cater to the growing demand for AI in industrial applications and robotics, particularly in Asia, according to the report.
"Nvidia's GPU is mainly suited for training, but we are seeing more newcomers developing chips which can target both training and inference," Sugiyama said.
Other companies entering the market include U.S.-based SambaNova Systems, backed by $SoftBank Group (9984.JP)$'s Vision Fund; Tenstorrent, founded by a former $Intel (INTC.US)$ engineer; and the British company Graphcore, recently acquired by $SoftBank (94345.JP)$.
Big tech companies like $Alphabet-C (GOOG.US)$, $Meta Platforms (META.US)$, and $Amazon (AMZN.US)$ Web Services are also joining in, along with Nvidia's rival $Advanced Micro Devices (AMD.US)$.
Why It Matters: The competition between Nvidia and emerging Asian startups is heating up as the AI chip market continues to expand. Recently, Eric Schmidt, former CEO of Google, highlighted Nvidia as a major player in the AI sector, noting that large tech companies are planning significant investments in Nvidia-based AI data centers, potentially costing up to $300 billion.
Meanwhile, SoftBank has faced setbacks in its efforts to rival Nvidia with its own AI chip production. Negotiations with Intel reportedly fell through due to Intel's inability to meet production demands, leading SoftBank to turn to $Taiwan Semiconductor (TSM.US)$, a key Nvidia supplier.
This story was generated using Benzinga Neuro and edited by Kaustubh Bagalkote
爲了挑戰NVIDIA公司(納斯達克股票代碼:NVDA)的人工智能主導地位,亞洲初創公司正在爲特定的人工智能應用開發更節能、更具成本效益的芯片。
發生了什麼:亞洲初創公司正在通過爲特定的人工智能應用開發更節能、更具成本效益的芯片來挑戰Nvidia的人工智能主導地位。日經亞洲週五報道,這些初創公司的目標是Nvidia的高能耗和龐大的設計留下的市場缺口。
這些初創公司專注於兩種類型的人工智能芯片:用於操作現有人工智能模型的 「推理」 芯片和 「訓練」 芯片,即用於開發新人工智能模型的高功率數據處理組件。
儘管Nvidia的GPU繼續在人工智能領域佔據主導地位,但初創公司認爲,他們的GPU的高能耗和龐大的設計在市場上留下了可以填補的空白。
這些初創公司認爲,Nvidia的GPU雖然強大,但對於許多應用程序來說過於耗能和昂貴。首選網絡(PFN)首席執行官西川徹表示:「沒有人想出完美的芯片架構來進行推理。」PFN正在開發旨在比Nvidia產品更高效、更便宜的芯片。
Nvidia 的 GPU 主要用於訓練人工智能模型,但它們的高成本和能耗使其不適用於筆記本電腦和可穿戴設備等設備。包括Omdia的杉山和宏在內的分析師認爲,對設備端人工智能的需求將增加,從而鼓勵新進入者進入市場。
由Sakyasingha Dasgupta領導的Edgecortix等初創公司正專注於解決諸如 「存儲牆」 問題之類的問題,以創建更精簡、更節能的人工智能芯片。報告稱,這些努力是更廣泛戰略的一部分,該戰略旨在滿足工業應用和機器人領域對人工智能不斷增長的需求,尤其是在亞洲。
杉山說:「Nvidia的GPU主要適用於訓練,但我們看到越來越多的新人開發出既可以針對訓練又可以推理的芯片。」
其他進入市場的公司包括由軟銀集團(場外交易代碼:SFTBY)(場外交易代碼:SFTBF)願景基金支持的總部位於美國的SambaNova Systems;由前英特爾公司(納斯達克股票代碼:INTC)工程師創立的Tenstorrent;以及最近被軟銀收購的英國公司Graphcore。
像Alphabet Inc這樣的大型科技公司。”s(納斯達克股票代碼:GOOG)(納斯達克股票代碼:GOOG)谷歌、Meta Platforms Inc.(納斯達克股票代碼:META)和亞馬遜公司(納斯達克股票代碼:AMZN)亞馬遜網絡服務也加入了行列,英偉達的競爭對手先進微設備公司(納斯達克股票代碼:AMD)。
爲何重要:隨着人工智能芯片市場的持續擴大,英偉達與亞洲新興初創公司之間的競爭正在升溫。最近,谷歌前首席執行官埃裏克·施密特強調英偉達是人工智能領域的主要參與者,並指出大型科技公司正計劃對基於NVIDIA的人工智能數據中心進行大量投資,可能耗資高達3000億美元。
同時,軟銀在通過自己的人工智能芯片生產與英偉達競爭的努力中遇到了挫折。據報道,由於英特爾無法滿足生產需求,與英特爾的談判破裂,導致軟銀求助於臺灣半導體制造有限公司。(紐約證券交易所代碼:TSM),英偉達的主要供應商。
- 特朗普時代的白宮官員安東尼·斯卡拉穆奇說,行業領袖希望兩黨對加密監管做出承諾:「我們不想與任何一方作戰」
圖片來自 Shutterstock
這個故事是使用 Benzinga Neuro 創作的,由 Kaustubh Bagalkote