Samsung doubles down on local AI with Nota AI—Exynos 2600 gets a turbocharge

Nota AI has secured a new supply agreement with Samsung Electronics Co., Ltd. for the upcoming Exynos 2600 mobile application processor, marking its second consecutive design win following integration in the Exynos 2500. The deal further embeds Nota AI’s optimization technology into Samsung’s on-device AI stack and reflects the growing demand for efficient generative AI performance without cloud dependence.

By powering the next iteration of Samsung’s Exynos AI Studio toolchain, Nota AI is positioning its NetsPresso platform as foundational infrastructure for running large-scale generative AI models directly on mobile devices. The agreement reinforces Nota AI’s standing as a core enabler of Samsung’s strategy to scale edge AI performance across smartphones, home appliances, and other embedded systems.

How is Nota AI’s optimization platform shaping the future of Samsung’s Exynos chipsets?

At the center of this collaboration is Nota AI’s proprietary NetsPresso® platform, which compresses and optimizes AI models for on-device deployment with up to 90 percent size reduction while maintaining high precision. That capability aligns with the increasing computational demands of generative AI and large language models being pushed to run locally on consumer hardware.

The Exynos 2600, Samsung’s upcoming mobile AP, is expected to integrate more advanced AI acceleration than its predecessor, making model efficiency a critical differentiator. By embedding Nota AI’s technology into Exynos AI Studio—a toolchain used by developers to build and deploy optimized AI models—the two companies are streamlining the ability to run advanced inference tasks, including large-scale text generation and image synthesis, without cloud support.

This offloads data center dependency, reduces latency, and improves privacy and battery efficiency for Samsung users. Importantly, it also allows Samsung Electronics to strengthen its differentiation against Qualcomm and Apple by highlighting vertically integrated AI toolchains as part of its semiconductor value proposition.

Why does the Exynos AI Studio upgrade matter for generative AI deployment at the edge?

Generative AI workloads have rapidly transitioned from data centers to edge environments as chipmakers and device manufacturers seek to reduce cost and power consumption while meeting real-time user expectations. However, most generative AI models—especially large transformer-based architectures—have high memory and compute requirements.

To address this, the upgraded Exynos AI Studio aims to deliver not just tooling flexibility, but automation. Nota AI is expected to contribute features that automate the end-to-end model optimization and deployment process, from quantization to pruning to hardware-specific compilation.

This makes it easier for developers to fine-tune large models and run them on-device with minimal technical overhead, a critical shift for software ecosystems that now expect plug-and-play AI integration. It also reduces time-to-market for new applications across Samsung’s device portfolio, especially in mobile, smart TVs, wearables, and smart home assistants.

The underlying shift is strategic: Samsung is transitioning from treating AI as a chipset feature to making model deployment and optimization a platform-level service that can be monetized across its hardware ecosystem. Nota AI’s tooling provides the technical abstraction necessary to execute that transition at scale.

What does this mean for Samsung’s positioning in the mobile AP and edge AI markets?

Samsung’s Exynos series has struggled in recent years to keep pace with Qualcomm’s Snapdragon dominance and Apple’s custom silicon innovation. But by doubling down on vertical AI integration—including model optimization pipelines—Samsung is betting that generative AI performance will become a primary battleground for AP competitiveness.

The inclusion of Nota AI’s platform since Exynos 2400 suggests a maturing partnership that is being operationalized across multiple chip generations. This goes beyond simple licensing. It implies a sustained co-development approach where optimization capabilities are being directly woven into Samsung’s AI toolchain roadmap.

For Samsung, this also reflects a shift in semiconductor strategy. Instead of competing solely on raw performance metrics like CPU and GPU clock speeds, the company is increasingly marketing its APs based on real-world AI performance—measured in inference speed, model fidelity, and energy efficiency.

This is especially timely as generative AI applications proliferate in mobile form factors, and as regulators and enterprise buyers demand more on-device processing for privacy, security, and cost reasons.

How is Nota AI leveraging this momentum to expand across sectors and geographies?

The Samsung deal marks a clear commercial validation of Nota AI’s lightweighting and optimization technology, but the company is also signaling broader ambitions. Nota AI emphasized that its solutions are increasingly being adopted across a range of industries—from robotics and healthcare to smart cities and education—often via integration with products from global manufacturers.

In many of these use cases, on-device inference is not a luxury but a necessity. Medical devices, autonomous robots, and industrial IoT systems often lack stable connectivity or require ultra-low latency, making cloud-based AI untenable. Nota AI’s ability to shrink models without degrading performance allows these verticals to benefit from modern AI without massive compute or memory overhead.

Additionally, the company’s announcement hints at commercialization efforts beyond just technical partnership. With Samsung as a reference customer, Nota AI may seek to deepen its footprint across the Android ecosystem and attract other hardware vendors building custom silicon.

What are the competitive implications for other players in edge and on-device AI?

For rivals in the edge AI stack, including Qualcomm, Apple, MediaTek, and Huawei, the Samsung–Nota AI collaboration underscores the growing importance of software-defined optimization capabilities. Hardware-level AI acceleration alone may not be sufficient without corresponding developer tooling and model orchestration platforms.

Companies like Qualcomm have invested heavily in tools such as AI Model Efficiency Toolkit (AIMET), and Apple has its proprietary Core ML framework. By contrast, Samsung’s Exynos AI Studio—supercharged by Nota AI—may allow more third-party developers and OEMs to optimize their own models and deploy them at scale, creating a more open, flexible development environment.

This could potentially attract AI startups and application developers seeking lower barriers to entry for on-device deployment, especially in regions where Samsung maintains a strong hardware footprint.

What are the financial and strategic risks for Nota AI as it scales?

While the Samsung partnership strengthens Nota AI’s credibility, it also increases the execution burden. Maintaining cross-generational integration with a global semiconductor player requires high tooling stability, backward compatibility, and robust developer support infrastructure.

Additionally, as more large-language and multimodal models emerge, the challenge will be to keep NetsPresso’s optimization engines compatible with increasingly complex architectures, including sparse models, MoEs (Mixture of Experts), and streaming transformer variants.

There is also platform concentration risk. If too much revenue or technical dependency stems from one partner—even a marquee one like Samsung—it may limit Nota AI’s ability to negotiate or expand horizontally. Diversification through cross-sector partnerships and licensing models will be key to sustaining long-term growth.

Still, with demand for efficient generative AI surging across sectors, the company is well-positioned to capitalize—provided it can maintain performance leadership and scale its platform without compromising ease of use.

What are the key takeaways from Nota AI’s expanded partnership with Samsung Electronics on Exynos 2600?

• Nota AI secured a back-to-back contract to supply model optimization tech for Samsung’s upcoming Exynos 2600 chip after delivering on Exynos 2500.

• The partnership deepens integration into Exynos AI Studio, positioning Nota AI’s NetsPresso platform as a core toolchain component.

• Optimization features will enable large generative AI models to run on-device, reducing reliance on cloud computing.

• Samsung is using vertical AI integration as a differentiator in its battle against Qualcomm and Apple in the mobile chipset market.

• Nota AI’s automation of compression, quantization, and deployment pipelines enhances developer productivity and time-to-market.

• The partnership may accelerate Nota AI’s expansion into sectors like robotics, healthcare, and smart infrastructure.

• Competitive pressure is rising across the edge AI stack as all major chipmakers race to enable high-performance local inference.

• Key risks for Nota AI include execution complexity, model compatibility challenges, and potential platform dependency on Samsung.