Nota（A486990）株式概要株式会社ノータは、AIモデル開発プラットフォームの設計・開発を行っています。 詳細A486990 ファンダメンタル分析スノーフレーク・スコア評価0/6将来の成長2/6過去の実績0/6財務の健全性5/6配当金0/6報酬収益は年間30%増加すると予測されています 過去 1 年間で収益は97.6%増加しましたリスク分析KR市場と比較した過去 3 か月間の株価の変動すべてのリスクチェックを見るA486990 Community Fair Values Create NarrativeSee what others think this stock is worth. Follow their fair value or set your own to get alerts.Your Fair Value₩Current Price₩25.05k1.0k% 割高 内在価値ディスカウントGrowth estimate overAnnual revenue growth rate5 Yearstime period%/yrDecreaseIncreasePastFuture-26b62b2016201920222025202620282031Revenue ₩61.7bEarnings ₩7.2bAdvancedSet Fair ValueView all narrativesNota Inc. 競合他社HancomSymbol: KOSDAQ:A030520Market cap: ₩465.0bMakinaRocksSymbol: KOSDAQ:A477850Market cap: ₩351.7bEMROSymbol: KOSDAQ:A058970Market cap: ₩253.4bSelvas AISymbol: KOSDAQ:A108860Market cap: ₩267.8b価格と性能株価の高値、安値、推移の概要Nota過去の株価現在の株価₩25,050.0052週高値₩65,300.0052週安値₩22,400.00ベータ01ヶ月の変化-9.73%3ヶ月変化-42.74%1年変化n/a3年間の変化n/a5年間の変化n/aIPOからの変化-19.19%最新ニュースお知らせ • 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.最新情報をもっと見る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...株主還元A486990KR SoftwareKR 市場7D-8.1%1.5%14.2%1Yn/a-21.1%180.3%株主還元を見る業界別リターン: A486990がKR Software業界に対してどのようなパフォーマンスを示したかを判断するにはデータが不十分です。リターン対市場: A486990 KR市場に対してどのようなパフォーマンスを示したかを判断するにはデータが不十分です。価格変動Is A486990's price volatile compared to industry and market?A486990 volatilityA486990 Average Weekly Movement15.0%Software Industry Average Movement9.3%Market Average Movement8.7%10% most volatile stocks in KR Market16.1%10% least volatile stocks in KR Market4.5%安定した株価: A486990の株価は、 KR市場と比較して過去 3 か月間で変動しています。時間の経過による変動: A486990の weekly volatility ( 15% ) は過去 1 年間安定していますが、依然としてKRの株式の 75% よりも高くなっています。会社概要設立従業員CEO（最高経営責任者ウェブサイト2015n/aMyungsu Chaewww.nota.ai株式会社ノータは、AIモデル開発プラットフォームの設計・開発を行っています。同社は、AIモデル開発と最適化のためのプラットフォーム「NetsPresso」、産業安全、監視、インテリジェント交通システムソリューションを提供するソリューションスイート「Nota Vision Agent」、ドライバー監視と顔認識システムを提供している。同社は2015年に法人化され、韓国のテジョンに本社を置いている。もっと見るNota Inc. 基礎のまとめNota の収益と売上を時価総額と比較するとどうか。A486990 基礎統計学時価総額₩564.61b収益（TTM）-₩15.02b売上高(TTM)₩16.62b32.2xP/Sレシオ-35.6xPER（株価収益率A486990 は割高か？公正価値と評価分析を参照収益と収入最新の決算報告書（TTM）に基づく主な収益性統計A486990 損益計算書（TTM）収益₩16.62b売上原価₩0売上総利益₩16.62bその他の費用₩31.63b収益-₩15.02b直近の収益報告Mar 31, 2026次回決算日該当なし一株当たり利益（EPS）-703.47グロス・マージン100.00%純利益率-90.36%有利子負債／自己資本比率12.6%A486990 の長期的なパフォーマンスは？過去の実績と比較を見るView Valuation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2026/06/18 23:34終値2026/06/18 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.
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.
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...
