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Edge AI Hardware Market Research Report 2025-2030

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The edge AI hardware market is expected to soar to USD 58.90 billion by 2030, from USD 26.14 billion in 2025, at a CAGR of 17.6%. The growth is fueled by the necessity for real-time data processing, improved latency, privacy, and bandwidth efficiency, alongside advancements in semiconductor technology, IoT, and smart devices. However, limitations include inadequate processing power and security concerns. Federated learning is driving a higher CAGR in training than inferred, while smartphones lead device use due to 5G. China dominates the Asia Pacific market, propelled by government support and tech innovation. The report includes insights from diverse industry experts and profiles key market players like Qualcomm, Huawei, and Samsung.

Global Edge AI Hardware Market

Global Edge AI Hardware Market
Global Edge AI Hardware Market

Dublin, Aug. 08, 2025 (GLOBE NEWSWIRE) — The “Edge AI Hardware Market by Device, Processor (CPU, GPU, and ASIC), Function, Power Consumption (Less than 1 W, 1-3 W, >3-5 W, >5-10 W, and More than 10 W), Vertical and Region – Global Forecast to 2030” report has been added to ResearchAndMarkets.com’s offering.

The edge AI hardware market is projected to reach USD 58.90 billion by 2030, up from USD 26.14 billion in 2025, at a CAGR of 17.6%

The report will help the market leaders/new entrants with information on the closest approximations of the revenue for the overall edge AI hardware market and the subsegments. The report will help stakeholders understand the competitive landscape and gain more insight to position their business better and plan suitable go-to-market strategies. The report also helps stakeholders understand the market’s pulse and provides information on key drivers, restraints, opportunities, and challenges.

The edge AI hardware market is growing due to the demand for real-time data processing, reduced latency, enhanced privacy, and better bandwidth efficiency. Advancements in semiconductor technology, IoT, autonomous vehicles, and smart devices also drive this growth.

However, the adoption has limitations, including the lack of processing power, memory, and energy available on edge devices to run complex AI models. This requires intense optimization that may negatively impact accuracy. Other limitations to adopting edge AI hardware in the vicinity included security vulnerabilities, deployment costs, scaling, and maintenance in distributed edge systems.

The report provides an-depth assessment of market shares, growth strategies, and offerings of leading players in the edge AI hardware market, such as Qualcomm Technologies, Inc. (US), Huawei Technologies Co., Ltd. (China), SAMSUNG (South Korea), Apple Inc. (US), and MediaTek Inc. (Taiwan), among others.

Training function to record higher CAGR during forecast period

The training functionality in edge AI will register a higher CAGR than the inference function as federated learning continues to take hold, wherein AI models can be trained on distributed edge devices directly. At the same time, data privacy and regulations can be complied with.



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MIT Researchers Develop AI Tool to Improve Flu Vaccine Strain Selection

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Insider Brief

  • MIT researchers have developed VaxSeer, an AI system that predicts which influenza strains will dominate and which vaccines will offer the best protection, aiming to reduce guesswork in seasonal flu vaccine selection.
  • Using deep learning on decades of viral sequences and lab data, VaxSeer outperformed the World Health Organization’s strain choices in 9 of 10 seasons for H3N2 and 6 of 10 for H1N1 in retrospective tests.
  • Published in Nature Medicine, the study suggests VaxSeer could improve vaccine effectiveness and may eventually be applied to other rapidly evolving health threats such as antibiotic resistance or drug-resistant cancers.

MIT researchers have unveiled an artificial intelligence tool designed to improve how seasonal influenza vaccines are chosen, potentially reducing the guesswork that often leaves health officials a step behind the fast-mutating virus.

The study, published in Nature Medicine, was authored by lead researcher Wenxian Shi along with Regina Barzilay, Jeremy Wohlwend, and Menghua Wu. It was supported in part by the U.S. Defense Threat Reduction Agency and MIT’s Jameel Clinic.

According to MIT, the system, called VaxSeer, was developed by scientists at MIT’s Computer Science and Artificial Intelligence Laboratory and the MIT Jameel Clinic for Machine Learning in Health. It uses deep learning models trained on decades of viral sequences and lab results to forecast which flu strains are most likely to dominate and how well candidate vaccines will work against them. Unlike traditional approaches that evaluate single mutations in isolation, VaxSeer’s large protein language model can capture the combined effects of multiple mutations and model shifting viral dominance more accurately.

“VaxSeer adopts a large protein language model to learn the relationship between dominance and the combinatorial effects of mutations,” Shi noted. “Unlike existing protein language models that assume a static distribution of viral variants, we model dynamic dominance shifts, making it better suited for rapidly evolving viruses like influenza.”

In retrospective tests covering ten years of flu seasons, VaxSeer’s strain recommendations outperformed those of the World Health Organization in nine of ten cases for H3N2 influenza, and in six of ten cases for H1N1, researchers said. In one notable example, the system correctly identified a strain for 2016 that the WHO did not adopt until the following year. Its predictions also showed strong correlation with vaccine effectiveness estimates reported by U.S., Canadian, and European surveillance networks.

The tool works in two parts: one model predicts which viral strains are most likely to spread, while another evaluates how effectively antibodies from vaccines can neutralize them in common hemagglutination inhibition assays. These predictions are then combined into a coverage score, which estimates the likely effectiveness of a candidate vaccine months before flu season begins.

“Given the speed of viral evolution, current therapeutic development often lags behind. VaxSeer is our attempt to catch up,” Barzilay noted.



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Analysis and Trading in One

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We had just discussed new crypto projects with AI integrations, and now ChadFi launches an AI terminal: analysis and trading in one. It is worth noting that the platform is at an early stage of development, but states that its AI-powered platform’s beta version already implements research, analysis, and execution in a single cycle.

They present the operational sequence as Data Collection – AI Analysis – Insights Generation – Trade Execution – Feedback Loop, that is, the analytical pipeline and the execution loop are closed within a single interface.

What Is the Actual Working Stage of the Platform?

At the moment, they state three core components: Deep Analysis engine, SpoonFed Setups, and All-in-One Execution. Thus, some functions are already available in the terminal, with the expansion of integrations with centralized venues planned for the next release.

Deep Analysis engine works across five data domains:

  • Technical indicators for the detection of chart patterns

  • Project fundamentals

  • On-chain flows and address activity

  • Social sentiment via X metrics

  • Smart money activity

Among the specific AI Analysis functions the platform offers:

  • AI-Powered Token Analysis

  • Personalized Entry and Exit Recommendations

  • Advanced Technical Analysis Tools

  • Real-Time Market Monitoring

  • Customizable Alerts and Notifications

  • Sentiment Analysis

AI has been helping major players in market analysis and decision-making long before this became popular and before AI-powered platforms began appearing every week. Learn more about the AI in Cryptocurrency Trading: Technical Review & Market Capabilities.

The output layer forms SpoonFed Setups – predefined scenarios for entry and position management that convert observations across multiple layers into actionable steps. The scenarios are then transferred into the execution loop, and the Feedback Loop feeds the result back into the analytics workspace.

Also, the platform states real-time whale monitoring, comprehensive wallet profiling taking into account historical performance and behavioral characteristics, visualization of liquidity movement between addresses, protocols, and segments, as well as observation of narrative rotation. These signals enter the overall pipeline and are used as one of the sources for SpoonFed Setups.

All-in-One Execution is designed for single-interface operation and supports multiple take-profit and stop-loss orders, and the built-in contract safety scanners serve as a mechanism for preliminary checks for common smart contract risks and the presence of dangerous patterns.

To make all this truly convenient for each individual user, their interface supports layout customization and a Customizable Dashboard with watchlists 2.0 and a set of widgets to assemble data layers on one screen for a specific task.

To avoid missing important signals, the Alerts and Notifications system is configured by conditions and delivery channels. For collaboration and social distribution, sharing of setups and interaction via X, Discord, and Telegram are supported.

A Good Initiative, but Is It a Worthy Product?

It is too early to state definitively. It is necessary to pass the beta stage and see how this will actually work at the level of a full-fledged system.

Also, they do not provide information about which AI models they use, how they train them, or how data management and model policies are arranged. If there are problems with the AI models, then one of the key functionalities of the platform would be eliminated.

However, even in this case, a focus on a customizable and detailed visibility toolkit, where the activity of influential addresses is concentrated and how market focus shifts across segments, can be valuable on its own. But again, only if data handling is implemented with genuine quality and reliability.



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Minus-AI Launches the Coolest Video Ad Agent for the AI Era

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Minus-AI

Singapore, Sept. 01, 2025 (GLOBE NEWSWIRE) — Minus-AI: The Coolest AI Video Ad Agent

 

https://youtu.be/HDyhkczn21k 

Minus-AI, a Singapore-based AI-native startup, has officially launched its breakthrough platform that transforms brand information into cinematic, multi-shot video ads in just minutes. Positioned at the intersection of AI marketing, content marketing, AI video ads, and AI video generation, Minus-AI is redefining how businesses of every size create and scale their marketing.

The company proudly states its vision in one bold slogan: “Minus-AI is the coolest AI video ad agent.”

(Frame generated by Minus AI)

A Startup with Momentum

Founded in late 2024, Minus-AI immediately attracted over one million USD in angel investment from renowned figures in the global film and entertainment industry. This rapid validation underscores both the technical depth of the team and the enormous demand for next-generation AI marketing solutions.

Minus-AI’s co-founders bring complementary expertise:

Dr. Luo, who previously served as Senior Principal Scientist at Autodesk Research, brings expertise in reinforcement learning and AI-driven creativity. His collaborations with creatives have been featured at various international venues. With Minus AI, he set out on a mission to build tools that harness the power of AI to enhance creative processes.

Ms. Cai, a graduate of New York University (NYU), was the founder of one of the earliest VR education startups in China, which quickly achieved profitability. With a background bridging creative technology and business execution, she now leads product and commercialization at Minus-AI.

Together, they represent the fusion of advanced AI research and creative entrepreneurship.

The Meaning of “Minus-AI”

As Dr. Luo explains, the name Minus-AI carries a philosophy:

“Minus-AI stands for reducing meaningless labor and leaving time for what truly matters. The dash in Minus-AI is also a minus sign — cutting away the unnecessary.”

This philosophy reflects the company’s mission: to simplify the complexity of content marketing, giving businesses a direct path from idea to finished ad, without wasted effort.

(Minus-AI logo design)

Five Core Advantages of Minus-AI

1. Trendy Ideas, Done for You

Most businesses struggle to keep up with fast-moving social media trends. Minus-AI solves this by embedding hotspots and viral formats directly into its system. From concept to creative format, the platform delivers fresh ideas already tailored to your product and the cultural moment.



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