View Future GrowthNota 過去の業績過去 基準チェック /06Notaは42.2%の年平均成長率で業績を伸ばしているが、Software業界はgrowingで10.8%毎年増加している。売上は成長しており、年平均97.6%の割合である。主要情報42.22%収益成長率76.80%EPS成長率Software 業界の成長14.56%収益成長率97.61%株主資本利益率-62.89%ネット・マージン-90.36%前回の決算情報31 Mar 2026最近の業績更新Reported Earnings • May 21First quarter 2026 earnings released: ₩188 loss per share (vs ₩573 loss in 1Q 2025)First quarter 2026 results: ₩188 loss per share (improved from ₩573 loss in 1Q 2025). Revenue: ₩3.58b (up ₩3.52b from 1Q 2025). Net loss: ₩4.02b (loss narrowed 29% from 1Q 2025). Revenue is forecast to grow 30% p.a. on average during the next 3 years, compared to a 16% growth forecast for the Software industry in South Korea.すべての更新を表示Recent updatesお知らせ • Jun 13Nota Ai Has Two Moe Quantization Papers Accepted At Icml 2026 WorkshopNota AI announced that two of its papers on MoE-specific quantization algorithms have been accepted to the Resource-Adaptive Foundation Model Inference (AdaptFM) Workshop at ICML 2026. The AdaptFM Workshop focuses on technologies that enable large-scale AI models to run efficiently under limited computing resources. This achievement is significant as it recognizes Nota AI's accumulated technical expertise in optimizing Mixture-of-Experts (MoE) models, an architecture increasingly regarded as a core structure for large language models (LLMs). MoE models improve both performance and efficiency by activating only a subset of expert models as needed. However, their complex structure requires a different approach to quantization, the process of making models smaller and more efficient, compared to conventional model architectures. Nota AI previously won both its track and the overall competition at the NVIDIA Nemotron Hackathon with a data-driven MoE quantization method. With the acceptance of these two papers, Nota AI will once again present research outcomes specifically designed for MoE architectures on a global research stage. The first accepted paper, "DREAM-MoE," proposes a method to reduce changes in a model's decision flow that can occur when large-scale AI models are quantized across multiple segments. The method focuses on the fact that even a small error in an earlier segment can affect expert selection in later segments. DREAM-MoE helps the quantized model select experts in a way that remains closer to the original model. The second paper, "SRA-MoE," proposes a method that identifies and prioritizes important inputs that have a greater impact on the model's final output. Rather than treating all inputs equally, SRA-MoE is designed to prevent expert selection from being significantly disrupted for these key inputs, helping maintain model quality more effectively under limited resources. Both studies demonstrated higher performance compared to the latest MoE-specific quantization methods. This shows that large-scale AI models can be executed with less memory and fewer computing resources while reducing quality degradation. Nota AI has been proactively focusing its R&D efforts on optimizing large AI models that require substantial memory and computing resources. The company is advancing large-scale model optimization, including Solar MoE, as part of the sovereign foundation model project led by the Upstage consortium. It is also expanding its experience in quantizing NVIDIA Nemotron 3 Nano to newer large models such as Nemotron Ultra, further broadening the scope of its optimization technologies. In addition, Nota AI will host "Nota AI - Korea Efficient Days" during ICML 2026 at COEX in Seoul. The event will bring together global researchers, engineers, and business leaders visiting Korea to share research trends and industrial applications of Efficient AI. Through the event, Nota AI plans to introduce its research achievements in large-scale AI model optimization and expand opportunities for technical collaboration and business engagement.New Risk • Jun 06New major risk - Share price stabilityThe company's share price has been highly volatile over the past 3 months. It is more volatile than 90% of South Korean stocks, typically moving 16% a week. This is considered a major risk. Share price volatility increases the risk of potential losses in the short-term as the stock tends to have larger drops in price more frequently than other stocks. It may also indicate the stock is highly sensitive to market conditions or economic conditions rather than being sensitive to its own business performance, which may also be inconsistent. This is currently the only risk that has been identified for the company.Reported Earnings • May 21First quarter 2026 earnings released: ₩188 loss per share (vs ₩573 loss in 1Q 2025)First quarter 2026 results: ₩188 loss per share (improved from ₩573 loss in 1Q 2025). Revenue: ₩3.58b (up ₩3.52b from 1Q 2025). Net loss: ₩4.02b (loss narrowed 29% from 1Q 2025). Revenue is forecast to grow 30% p.a. on average during the next 3 years, compared to a 16% growth forecast for the Software industry in South Korea.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...収支内訳Nota の稼ぎ方とお金の使い方。LTMベースの直近の報告された収益に基づく。収益と収入の歴史KOSDAQ:A486990 収益、費用、利益 ( )KRW Millions日付収益収益G+A経費研究開発費31 Mar 2616,619-15,01628,84972831 Dec 2513,101-16,63025,74365630 Sep 2510,223-25,72821,89273530 Jun 258,817-25,44418,95197031 Mar 258,410-25,98918,1521,02431 Dec 248,437-24,85217,38395731 Dec 233,581-13,13511,7732,002質の高い収益: A486990は現在利益が出ていません。利益率の向上: A486990は現在利益が出ていません。フリー・キャッシュフローと収益の比較過去の収益成長分析収益動向: A486990の過去 5 年間の前年比収益成長率がプラスであったかどうかを判断するにはデータが不十分です。成長の加速: A486990の過去 1 年間の収益成長を 5 年間の平均と比較することはできません。現在は利益が出ていないためです。収益対業界: A486990は利益が出ていないため、過去 1 年間の収益成長をSoftware業界 ( 21.7% ) と比較することは困難です。株主資本利益率高いROE: A486990は現在利益が出ていないため、自己資本利益率 ( -62.89% ) はマイナスです。総資産利益率使用総資本利益率過去の好業績企業の発掘7D1Y7D1Y7D1YSoftware 、過去の業績が好調な企業。View Financial Health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2026/06/20 20:41終値2026/06/19 00:00収益2026/03/31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この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、レポートの使い方に関する ガイド や YouTube の チュートリアル もご用意しています。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. 2 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.
Reported Earnings • May 21First quarter 2026 earnings released: ₩188 loss per share (vs ₩573 loss in 1Q 2025)First quarter 2026 results: ₩188 loss per share (improved from ₩573 loss in 1Q 2025). Revenue: ₩3.58b (up ₩3.52b from 1Q 2025). Net loss: ₩4.02b (loss narrowed 29% from 1Q 2025). Revenue is forecast to grow 30% p.a. on average during the next 3 years, compared to a 16% growth forecast for the Software industry in South Korea.
お知らせ • Jun 13Nota Ai Has Two Moe Quantization Papers Accepted At Icml 2026 WorkshopNota AI announced that two of its papers on MoE-specific quantization algorithms have been accepted to the Resource-Adaptive Foundation Model Inference (AdaptFM) Workshop at ICML 2026. The AdaptFM Workshop focuses on technologies that enable large-scale AI models to run efficiently under limited computing resources. This achievement is significant as it recognizes Nota AI's accumulated technical expertise in optimizing Mixture-of-Experts (MoE) models, an architecture increasingly regarded as a core structure for large language models (LLMs). MoE models improve both performance and efficiency by activating only a subset of expert models as needed. However, their complex structure requires a different approach to quantization, the process of making models smaller and more efficient, compared to conventional model architectures. Nota AI previously won both its track and the overall competition at the NVIDIA Nemotron Hackathon with a data-driven MoE quantization method. With the acceptance of these two papers, Nota AI will once again present research outcomes specifically designed for MoE architectures on a global research stage. The first accepted paper, "DREAM-MoE," proposes a method to reduce changes in a model's decision flow that can occur when large-scale AI models are quantized across multiple segments. The method focuses on the fact that even a small error in an earlier segment can affect expert selection in later segments. DREAM-MoE helps the quantized model select experts in a way that remains closer to the original model. The second paper, "SRA-MoE," proposes a method that identifies and prioritizes important inputs that have a greater impact on the model's final output. Rather than treating all inputs equally, SRA-MoE is designed to prevent expert selection from being significantly disrupted for these key inputs, helping maintain model quality more effectively under limited resources. Both studies demonstrated higher performance compared to the latest MoE-specific quantization methods. This shows that large-scale AI models can be executed with less memory and fewer computing resources while reducing quality degradation. Nota AI has been proactively focusing its R&D efforts on optimizing large AI models that require substantial memory and computing resources. The company is advancing large-scale model optimization, including Solar MoE, as part of the sovereign foundation model project led by the Upstage consortium. It is also expanding its experience in quantizing NVIDIA Nemotron 3 Nano to newer large models such as Nemotron Ultra, further broadening the scope of its optimization technologies. In addition, Nota AI will host "Nota AI - Korea Efficient Days" during ICML 2026 at COEX in Seoul. The event will bring together global researchers, engineers, and business leaders visiting Korea to share research trends and industrial applications of Efficient AI. Through the event, Nota AI plans to introduce its research achievements in large-scale AI model optimization and expand opportunities for technical collaboration and business engagement.
New Risk • Jun 06New major risk - Share price stabilityThe company's share price has been highly volatile over the past 3 months. It is more volatile than 90% of South Korean stocks, typically moving 16% a week. This is considered a major risk. Share price volatility increases the risk of potential losses in the short-term as the stock tends to have larger drops in price more frequently than other stocks. It may also indicate the stock is highly sensitive to market conditions or economic conditions rather than being sensitive to its own business performance, which may also be inconsistent. This is currently the only risk that has been identified for the company.
Reported Earnings • May 21First quarter 2026 earnings released: ₩188 loss per share (vs ₩573 loss in 1Q 2025)First quarter 2026 results: ₩188 loss per share (improved from ₩573 loss in 1Q 2025). Revenue: ₩3.58b (up ₩3.52b from 1Q 2025). Net loss: ₩4.02b (loss narrowed 29% from 1Q 2025). Revenue is forecast to grow 30% p.a. on average during the next 3 years, compared to a 16% growth forecast for the Software industry in South Korea.
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...