View ValuationNota 将来の成長Future 基準チェック /26 Notaは収益が増加すると予測されています。主要情報n/a収益成長率n/aEPS成長率Software 収益成長31.8%収益成長率31.5%将来の株主資本利益率6.10%アナリストカバレッジLow最終更新日12 Mar 2026今後の成長に関する最新情報更新なしすべての更新を表示Recent updatesお知らせ • Mar 17Nota Inc., Annual General Meeting, Mar 31, 2026Nota Inc., Annual General Meeting, Mar 31, 2026, at 10:00 Tokyo Standard Time. Location: conference room, 1, expo-ro, yuseong-gu, daejeon South Koreaお知らせ • Mar 10Nota AI Showcases End-To-End On-Device AI At Embedded World 2026Nota AI, an AI optimization technology company, announced that it will participate in Embedded World 2026, taking place March 10-12 in Nuremberg, Germany. At the event, the company will present the full lifecycle of on-device AI-from model optimization to deployment in real-world industrial environments. Nota AI will showcase how AI models are optimized through NetsPresso® and deployed across a wide range of global hardware platforms before being implemented in real industrial environments. Nota AI will demonstrate how semiconductor companies can rapidly optimize high-performance AI models for their chips using its AI model optimization platform, NetsPresso®. The company has accumulated extensive expertise in lightweighting and optimizing AI models—from small language models (SLMs) to large language models (LLMs) and vision-language models (VLMs). To date, Nota AI has successfully compressed more than 40 AI models while maintaining performance and has deployed its optimization technologies across over 100 hardware devices. The company recently supplied AI optimization technology for Samsung Electronics' Exynos 2600, where the technology serves as a core component enabling mobile on-device AI capabilities. Nota AI has also maintained ongoing technology collaborations with global semiconductor companies including Qualcomm and Arm. At Embedded World, the company will present live demonstrations showing both computer vision models and large language models running in real time on these hardware platforms, highlighting AI performance in edge environments. Nota AI will also showcase a Device Farm, featuring a collection of hardware platforms optimized by the company over the past decade. Visitors will be able to explore a range of chipsets from major global semiconductor companies running AI models optimized with Nota AI's technology, demonstrating the company's experience in optimizing more than 100 hardware platforms over the past ten years. Nota AI will introduce real-world solutions that combine AI models with hardware optimization in on-device environments. Through its video analytics solution NVA (Nota Vision Agent), Nota AI has delivered technologies across industries such as safety monitoring, security, and smart city operations in collaboration with global partners including NVIDIA. At the booth, the company will demonstrate real deployment cases including selective video monitoring, intelligent transportation systems (ITS), and industrial safety monitoring. Nota AI will also present its latest research achievements recently accepted at ICLR 2026 and the AAAI 2026 Foundation Model Workshop. Both studies focus on improving the efficiency and reliability of vision-language models (VLMs), highlighting Nota AI's technological capabilities across the broader physical AI landscape-from vision-language models to vision-language-action (VLA) systems. During the exhibition, Tae-Ho Kim, CTO & Co-Founder of Nota AI, will host mini sessions at the booth to present the company's AI lightweighting and optimization strategies and share real-world cases of applying Nota AI technologies to global semiconductor platforms. Nota AI will offer complimentary Embedded World visitor passes to attendees who pre-register through the company's official website and visit the Nota booth (Hall 5, Booth 5-422).お知らせ • Mar 06Nota AI Announces Proprietary Quantization Technology For Upstage Solar LLMNota AI, an AI optimization technology company behind the Nota AI brand, announced that it has developed a next-generation quantization technology that significantly compresses the size of Solar, a high-performance large language model (LLM) developed by Upstage, while maintaining high accuracy. The breakthrough reduces inference costs and improves processing speed without sacrificing performance. The development was carried out as part of the Sovereign AI Foundation Model Project led by South Korea's Ministry of Science and ICT. By applying Nota AI's lightweighting and optimization technologies to Solar Open 100B, the company significantly improved memory efficiency while preserving model performance. The achievement lowers the memory requirements of the 100B-parameter model while maintaining its capabilities, enabling more practical deployment of Korean AI foundation models in physical AI environments such as mobility and robotics. The newly developed technology focuses on addressing technical challenges associated with the Mixture of Experts (MoE) architecture, which is rapidly gaining adoption in next-generation LLMs. Conventional quantization methods typically compress the entire model uniformly without considering the distinct characteristics of individual expert models. To overcome this limitation, Nota AI developed a proprietary algorithm optimized for MoE architectures, called Nota AI MoE Quantization. The approach is designed to minimize quantization distortion during the inference process of MoE models. Unlike conventional methods that uniformly reduce precision across all operations, Nota AI's algorithm selectively preserves precision in critical components while compressing less sensitive parts of the model. This enables effective model compression while minimizing performance loss. Applying the technology to the Solar 100B model yielded significant improvements compared with conventional quantization methods. Nota AI successfully reduced Solar's memory usage from 191.2GB to 51.9GB, representing a 72.8% reduction. At the same time, the model maintained performance levels comparable to the original version, achieving a Perplexity (PPL) score of 6.81, close to the baseline model's 6.06. In contrast, some generic quantization approaches resulted in performance degradation exceeding fivefold. Nota AI has filed a patent application for the technology to strengthen its intellectual property portfolio. While conventional quantization techniques often sacrifice model performance to reduce memory usage, Nota AI's technology demonstrates that it is possible to maintain performance while delivering AI services faster and to more users on limited GPU infrastructure. As a result, enterprises can deploy large-scale LLMs more easily on their own devices—models that were previously difficult to implement due to hardware constraints. The significant reduction in Solar 100B's memory footprint while preserving performance also creates new opportunities for deploying high-performance AI in real-world on-device environments, including robotics and automotive systems. Additionally, the technology enables organizations facing limited access to high-end GPU infrastructure to serve more users on the same hardware, directly contributing to lower operational costs.分析記事 • Feb 03Health Check: How Prudently Does Nota (KOSDAQ:486990) Use Debt?David Iben put it well when he said, 'Volatility is not a risk we care about. What we care about is avoiding the...業績と収益の成長予測KOSDAQ:A486990 - アナリストの将来予測と過去の財務データ ( )KRW Millions日付収益収益フリー・キャッシュフロー営業活動によるキャッシュ平均アナリスト数12/31/202844,223N/AN/AN/A212/31/202733,295N/AN/AN/A312/31/202625,400N/AN/AN/A112/31/202513,101-16,630-12,237-11,788N/A9/30/202510,223-25,728-12,158-11,800N/A6/30/20258,817-25,444-12,160-11,842N/A3/31/20258,627-25,148-12,075-11,318N/A12/31/20248,437-24,852-11,989-10,794N/A12/31/20233,581-13,135-8,133-7,995N/Aアナリストによる今後の成長予測収入対貯蓄率: A486990の予測収益成長が 貯蓄率 ( 3.1% ) を上回っているかどうかを判断するにはデータが不十分です。収益対市場: A486990の収益がKR市場よりも速く成長すると予測されるかどうかを判断するにはデータが不十分です高成長収益: A486990の収益が今後 3 年間で 大幅に 増加すると予想されるかどうかを判断するにはデータが不十分です。収益対市場: A486990の収益 ( 31.5% ) KR市場 ( 12.4% ) よりも速いペースで成長すると予測されています。高い収益成長: A486990の収益 ( 31.5% ) 20%よりも速いペースで成長すると予測されています。一株当たり利益成長率予想将来の株主資本利益率将来のROE: A486990の 自己資本利益率 は、3年後には低くなると予測されています ( 6.1 %)。成長企業の発掘7D1Y7D1Y7D1YSoftware 業界の高成長企業。View Past Performance企業分析と財務データの現状データ最終更新日（UTC時間）企業分析2026/05/07 18:00終値2026/05/07 00:00収益2025/12/31年間収益2025/12/31データソース企業分析に使用したデータはS&P Global Market Intelligence LLC のものです。本レポートを作成するための分析モデルでは、以下のデータを使用しています。データは正規化されているため、ソースが利用可能になるまでに時間がかかる場合があります。パッケージデータタイムフレーム米国ソース例会社財務10年損益計算書キャッシュ・フロー計算書貸借対照表SECフォーム10-KSECフォーム10-Qアナリストのコンセンサス予想+プラス3年予想財務アナリストの目標株価アナリストリサーチレポートBlue Matrix市場価格30年株価配当、分割、措置ICEマーケットデータSECフォームS-1所有権10年トップ株主インサイダー取引SECフォーム4SECフォーム13Dマネジメント10年リーダーシップ・チーム取締役会SECフォーム10-KSECフォームDEF 14A主な進展10年会社からのお知らせSECフォーム8-K* 米国証券を対象とした例であり、非米国証券については、同等の規制書式および情報源を使用。特に断りのない限り、すべての財務データは1年ごとの期間に基づいていますが、四半期ごとに更新されます。これは、TTM（Trailing Twelve Month）またはLTM（Last Twelve Month）データとして知られています。詳細はこちら。分析モデルとスノーフレーク本レポートを生成するために使用した分析モデルの詳細は当社のGithubページでご覧いただけます。また、レポートの使用方法に関するガイドやYoutubeのチュートリアルも掲載しています。シンプリー・ウォールストリート分析モデルを設計・構築した世界トップクラスのチームについてご紹介します。業界およびセクターの指標私たちの業界とセクションの指標は、Simply Wall Stによって6時間ごとに計算されます。アナリスト筋Nota Inc. 3 これらのアナリストのうち、弊社レポートのインプットとして使用した売上高または利益の予想を提出したのは、 。アナリストの投稿は一日中更新されます。3 アナリスト機関Eun Jung ShinDB Financial Investment Co. Ltd.Jongsun ParkEugene Investment & Securities Co Ltd.Jun Ki BaekNH Investment & Securities Co., Ltd.
お知らせ • Mar 17Nota Inc., Annual General Meeting, Mar 31, 2026Nota Inc., Annual General Meeting, Mar 31, 2026, at 10:00 Tokyo Standard Time. Location: conference room, 1, expo-ro, yuseong-gu, daejeon South Korea
お知らせ • Mar 10Nota AI Showcases End-To-End On-Device AI At Embedded World 2026Nota AI, an AI optimization technology company, announced that it will participate in Embedded World 2026, taking place March 10-12 in Nuremberg, Germany. At the event, the company will present the full lifecycle of on-device AI-from model optimization to deployment in real-world industrial environments. Nota AI will showcase how AI models are optimized through NetsPresso® and deployed across a wide range of global hardware platforms before being implemented in real industrial environments. Nota AI will demonstrate how semiconductor companies can rapidly optimize high-performance AI models for their chips using its AI model optimization platform, NetsPresso®. The company has accumulated extensive expertise in lightweighting and optimizing AI models—from small language models (SLMs) to large language models (LLMs) and vision-language models (VLMs). To date, Nota AI has successfully compressed more than 40 AI models while maintaining performance and has deployed its optimization technologies across over 100 hardware devices. The company recently supplied AI optimization technology for Samsung Electronics' Exynos 2600, where the technology serves as a core component enabling mobile on-device AI capabilities. Nota AI has also maintained ongoing technology collaborations with global semiconductor companies including Qualcomm and Arm. At Embedded World, the company will present live demonstrations showing both computer vision models and large language models running in real time on these hardware platforms, highlighting AI performance in edge environments. Nota AI will also showcase a Device Farm, featuring a collection of hardware platforms optimized by the company over the past decade. Visitors will be able to explore a range of chipsets from major global semiconductor companies running AI models optimized with Nota AI's technology, demonstrating the company's experience in optimizing more than 100 hardware platforms over the past ten years. Nota AI will introduce real-world solutions that combine AI models with hardware optimization in on-device environments. Through its video analytics solution NVA (Nota Vision Agent), Nota AI has delivered technologies across industries such as safety monitoring, security, and smart city operations in collaboration with global partners including NVIDIA. At the booth, the company will demonstrate real deployment cases including selective video monitoring, intelligent transportation systems (ITS), and industrial safety monitoring. Nota AI will also present its latest research achievements recently accepted at ICLR 2026 and the AAAI 2026 Foundation Model Workshop. Both studies focus on improving the efficiency and reliability of vision-language models (VLMs), highlighting Nota AI's technological capabilities across the broader physical AI landscape-from vision-language models to vision-language-action (VLA) systems. During the exhibition, Tae-Ho Kim, CTO & Co-Founder of Nota AI, will host mini sessions at the booth to present the company's AI lightweighting and optimization strategies and share real-world cases of applying Nota AI technologies to global semiconductor platforms. Nota AI will offer complimentary Embedded World visitor passes to attendees who pre-register through the company's official website and visit the Nota booth (Hall 5, Booth 5-422).
お知らせ • Mar 06Nota AI Announces Proprietary Quantization Technology For Upstage Solar LLMNota AI, an AI optimization technology company behind the Nota AI brand, announced that it has developed a next-generation quantization technology that significantly compresses the size of Solar, a high-performance large language model (LLM) developed by Upstage, while maintaining high accuracy. The breakthrough reduces inference costs and improves processing speed without sacrificing performance. The development was carried out as part of the Sovereign AI Foundation Model Project led by South Korea's Ministry of Science and ICT. By applying Nota AI's lightweighting and optimization technologies to Solar Open 100B, the company significantly improved memory efficiency while preserving model performance. The achievement lowers the memory requirements of the 100B-parameter model while maintaining its capabilities, enabling more practical deployment of Korean AI foundation models in physical AI environments such as mobility and robotics. The newly developed technology focuses on addressing technical challenges associated with the Mixture of Experts (MoE) architecture, which is rapidly gaining adoption in next-generation LLMs. Conventional quantization methods typically compress the entire model uniformly without considering the distinct characteristics of individual expert models. To overcome this limitation, Nota AI developed a proprietary algorithm optimized for MoE architectures, called Nota AI MoE Quantization. The approach is designed to minimize quantization distortion during the inference process of MoE models. Unlike conventional methods that uniformly reduce precision across all operations, Nota AI's algorithm selectively preserves precision in critical components while compressing less sensitive parts of the model. This enables effective model compression while minimizing performance loss. Applying the technology to the Solar 100B model yielded significant improvements compared with conventional quantization methods. Nota AI successfully reduced Solar's memory usage from 191.2GB to 51.9GB, representing a 72.8% reduction. At the same time, the model maintained performance levels comparable to the original version, achieving a Perplexity (PPL) score of 6.81, close to the baseline model's 6.06. In contrast, some generic quantization approaches resulted in performance degradation exceeding fivefold. Nota AI has filed a patent application for the technology to strengthen its intellectual property portfolio. While conventional quantization techniques often sacrifice model performance to reduce memory usage, Nota AI's technology demonstrates that it is possible to maintain performance while delivering AI services faster and to more users on limited GPU infrastructure. As a result, enterprises can deploy large-scale LLMs more easily on their own devices—models that were previously difficult to implement due to hardware constraints. The significant reduction in Solar 100B's memory footprint while preserving performance also creates new opportunities for deploying high-performance AI in real-world on-device environments, including robotics and automotive systems. Additionally, the technology enables organizations facing limited access to high-end GPU infrastructure to serve more users on the same hardware, directly contributing to lower operational costs.
分析記事 • Feb 03Health Check: How Prudently Does Nota (KOSDAQ:486990) Use Debt?David Iben put it well when he said, 'Volatility is not a risk we care about. What we care about is avoiding the...