Samsung bets on Nota AI to bring high-performance AI to smartphones

Nota AI, a South Korea-based specialist in AI model compression and deployment, has announced a technology collaboration with Samsung Electronics Co., Ltd. that could redefine how generative AI is experienced on mobile devices. The partnership centers on the integration of Nota AI’s optimization software into Samsung Electronics’ Exynos AI Studio, a core toolchain for developing and deploying AI models on Exynos chips. This integration is debuting with Samsung Electronics’ next-generation application processor, the Exynos 2500.

The Exynos 2500, slated for use in upcoming Galaxy smartphones, is being positioned by Samsung Electronics as a flagship processor with powerful on-device generative AI capabilities. The agreement gives developers access to a streamlined pipeline for building and optimizing AI models directly on-chip, without the need for cloud computing infrastructure. The goal is to deliver high-performance generative AI that is faster, more secure, and accessible even in offline environments.

The strategic collaboration represents a turning point for both companies. Samsung Electronics is doubling down on localized AI processing, while Nota AI is bringing its optimization engine to a global commercial stage through a mass-market product with high distribution scale. With this partnership, the two South Korean technology companies are aiming to deliver advanced mobile AI use cases such as offline AI assistants, translation, augmented camera intelligence, and generative content creation, all powered natively on mobile hardware.

How is Samsung Electronics using Exynos 2500 to lead the shift toward embedded AI intelligence?

Samsung Electronics has made clear that it intends to lead the next evolution of mobile computing by embedding AI functionality directly into its chipsets. By integrating Nota AI’s model optimization stack into Exynos AI Studio, Samsung Electronics is taking a significant step toward building a high-performance, on-device generative AI experience that no longer relies on remote data centers.

Chulmin Jo, Vice President of Mobile Application Processor Software Development at Samsung Electronics, noted that the integration of Nota AI’s technology into the toolchain has substantially increased the efficiency of AI model development when compared to previous generations. While Jo did not provide quantitative metrics, his statement points to a measurable improvement in developer experience and model performance.

This comes at a time when mobile manufacturers are increasingly emphasizing AI as a core part of their user experience strategy. The push toward NPUs, edge AI, and secure offline execution has become central to competitiveness in the smartphone market. With Exynos 2500, Samsung Electronics is delivering not just a faster chip, but a full-stack platform that can support sophisticated AI workflows on-device.

The ability to deploy complex language models and vision-based AI without internet connectivity could fundamentally reshape how features like AI assistants, voice transcription, and intelligent camera functions operate on Samsung Electronics’ future phones.

Why is this partnership a defining commercial milestone for Nota AI’s global ambitions?

For Nota AI, the Samsung Electronics collaboration represents one of the most important milestones in the company’s transition from a research-driven startup to a globally scaled AI platform vendor. CEO Myungsu Chae explained that the partnership extends beyond a simple licensing model and into a full-fledged integration of hardware and software. By embedding Nota AI’s optimization framework into a toolchain used by global smartphone developers, the company is proving that its compression technology is ready for widespread, real-time deployment.

Unlike many AI startups that focus solely on cloud-based inference acceleration or enterprise tooling, Nota AI has bet heavily on making generative AI work directly on endpoints such as smartphones and embedded systems. The Exynos AI Studio integration offers real-world proof of this strategy, as developers using Samsung Electronics’ chipsets now have access to more efficient and battery-friendly ways to deploy AI models.

Analysts tracking the edge AI segment have described this deal as a validation of Nota AI’s product-market fit. While the company has long claimed to deliver best-in-class compression without loss of model accuracy, the true test has always been performance in mass-market environments. The Exynos 2500 deployment provides that credibility.

What does Nota AI’s post-IPO strategy suggest about its path toward global edge AI leadership?

Nota AI’s roadmap has been closely watched since its successful listing on the Korea Exchange (KOSDAQ) via the technology-special track. The IPO has given the company fresh capital to scale its operations beyond South Korea and accelerate its expansion into strategic global markets such as North America, Europe, and the Middle East.

The firm’s investor list includes some of South Korea’s most prominent technology players. Samsung Electronics, LG, NAVER D2SF, and Kakao are among the institutions that have invested in Nota AI, signaling strong domestic confidence in the company’s IP and commercialization potential. These alliances also offer partnership leverage across sectors ranging from smartphones and smart home devices to automotive and industrial automation.

Chae has stated that the company intends to aggressively pursue commercial partnerships that replicate the Exynos integration model with other global chipmakers and device OEMs. Analysts expect the firm’s next wave of partnerships could extend into AI-enhanced automotive systems, wearables, and industrial edge devices that need real-time decision-making without reliance on cloud services.

Could this be the blueprint for how generative AI scales offline in smartphones?

Generative AI is now being deployed across a growing number of consumer applications, but the challenge has always been making these models run efficiently on devices with limited processing power and battery life. The push toward edge AI has accelerated as privacy concerns, network latency, and cost constraints make full cloud dependency impractical.

Samsung Electronics, through its Exynos line of processors, is aligning itself with the new generation of AI-native chips. In this context, the integration of a lean optimization framework such as Nota AI’s becomes a critical differentiator. Instead of relying on ultra-large models hosted in the cloud, Samsung Electronics can now enable hybrid AI workflows where smaller, compressed models execute critical tasks on-device.

Industry experts suggest that such hybrid models will be standard by 2026, with inference tasks split between on-device execution and cloud fallback only when needed. With the Exynos 2500 processor, Samsung Electronics is laying the groundwork for this shift by offering a complete toolkit for developers to build such experiences.

For Nota AI, this is not just a single integration, it is a platform validation at scale. The fact that its technology has passed through Samsung Electronics’ stringent development and integration process means it could now be positioned as the default optimization layer for other devices requiring high-performance AI at the edge.

What are the strategic implications for global OEMs competing in the AI-enabled smartphone market?

Samsung Electronics’ latest move reinforces how the competition in mobile devices is shifting from display or camera enhancements to embedded intelligence. The emergence of AI-accelerated chipsets has changed the battleground, and players such as Apple, Google, and Qualcomm are already embedding advanced AI capabilities into their chips. However, the integration of third-party model optimization like that from Nota AI offers Samsung Electronics a path to stay ahead without depending solely on internal software development.

Global OEMs seeking to bring AI capabilities to mid-range and entry-level devices could also benefit from such integration layers, especially in markets with less consistent network access. If edge AI becomes the new baseline expectation among consumers, then optimization layers like Nota AI’s could become a critical enabler of product differentiation.

As the mobile industry braces for an AI-native design cycle, the Nota AI–Samsung Electronics partnership may come to be seen as a blueprint for how performance, efficiency, and market readiness intersect in the age of embedded generative intelligence.

What are the key takeaways from Nota AI’s collaboration with Samsung Electronics on Exynos 2500?

• Nota AI has signed a technology collaboration agreement with Samsung Electronics Co., Ltd. to embed its AI optimization technology into the Exynos AI Studio toolchain.

• The integration debuts on the Exynos 2500 application processor, enabling high-performance on-device generative AI experiences without relying on cloud infrastructure.

• Samsung Electronics executives highlighted the efficiency gains in AI model development, positioning Exynos 2500 as a core platform for next-gen mobile intelligence.

• Nota AI’s framework compresses and optimizes machine learning models, reducing power consumption while enhancing performance for smartphone applications.

• This deal represents the first large-scale commercial deployment of Nota AI’s platform into mass-market consumer devices, validating its scalability.

• CEO Myungsu Chae confirmed the company will continue building an ecosystem of edge AI deployments through partnerships beyond Samsung Electronics.

• Following its KOSDAQ IPO, Nota AI is expanding into North America, Europe, and the Middle East with a roadmap focused on embedded AI for mobile, automotive, and industrial use.

• Institutional investors including Samsung Electronics, LG, NAVER D2SF, and Kakao back Nota AI, reinforcing its status as a key player in edge AI infrastructure.

• Industry analysts view this deal as a strategic move for Samsung Electronics to compete in AI-enabled chipsets by optimizing performance at the device level.

• The collaboration reflects a broader industry shift toward hybrid and offline generative AI models that prioritize speed, privacy, and energy efficiency on mobile platforms.