Technologies For decades, classic science-fiction films imagined artificial intelligence as towering, room-sized computers filled with blinking lights and humming machinery. AI was something distant, enormous, and mysterious — a technology accessible only to scientists and supercomputers. Today, that image has been completely transformed. AI lives not in massive labs, but in the smartphones people carry and the appliances they use every day. This shift is largely driven by the rapid advancement of on-device AI, a technology that brings cloud-level intelligence directly to personal devices.
Samsung Electronics is at the forefront of this transformation. The company has been expanding the application of on-device AI across a growing range of products, from Galaxy smartphones and tablets to home appliances equipped with smarter, more adaptive features. Rather than relying solely on external servers or cloud processing, these devices can now perform increasingly complex AI tasks locally. This change allows users to enjoy faster, more private, and more energy-efficient AI experiences wherever they are.
However, the journey to true on-device AI is far from simple. Traditional AI systems, especially the large language models (LLMs) that power modern generative AI, depend on enormous computational resources. They typically run on server clusters equipped with specialized accelerators and vast memory capacity. Smartphones and consumer devices, on the other hand, operate under strict limits: limited RAM, lower power budgets, and thermal constraints. Delivering advanced AI under these conditions requires highly optimized models, smarter processing strategies, and new generations of lightweight AI architecture.
To overcome these challenges, researchers at Samsung Research’s AI Center are developing foundational technologies that allow large AI models to run efficiently on small devices. Their work spans several areas — including model compression, software-level optimization for AI runtimes, and the creation of entirely new model architectures built specifically for on-device environments.
Samsung Newsroom recently spoke with Dr. MyungJoo Ham, Master at the AI Center within Samsung Research, to explore how these technologies are evolving and what the future holds for on-device AI. Dr. Ham, who has spent years working on system software and AI optimization, shared insights on how generative AI can be miniaturized without compromising quality, and why this field is becoming essential for next-generation devices.
The First Step Toward On-Device AI
At the core of modern generative AI are large language models — the engines responsible for understanding user queries, generating natural responses, summarizing information, and more. These models can contain billions of parameters and typically rely on 32-bit floating-point numerical representations. Running such enormous models on compact devices, however, poses a major problem.
“Running a highly advanced model that performs billions of computations directly on a smartphone or laptop would quickly drain the battery, increase heat and slow response times — noticeably degrading the user experience,” Dr. Ham explained. “Model compression technology emerged to address these issues.”
Model compression is a broad field that includes techniques like pruning, distillation, and quantization. Among these, quantization has become one of the most essential techniques in the push toward on-device AI.
Why Quantization Matters
LLMs rely on extremely high-precision numerical calculations. These calculations are typically handled in 32-bit floating point (FP32). While this precision is valuable for accuracy, it is unnecessary for most real-world AI tasks. Quantization reduces the precision of these numbers — often from FP32 to 8-bit or even 4-bit integer formats — dramatically reducing memory requirements and computational overhead.
Dr. Ham compared this process to compressing an image: “It’s like compressing a high-resolution photo so the file size shrinks but the visual quality remains nearly the same,” he said. “For instance, converting 32-bit floating-point calculations to 8-bit or even 4-bit integers significantly reduces memory use and computational load, speeding up response times.”
By lowering precision, AI models become smaller and faster, enabling them to fit into the memory footprint of a mobile device while still producing high-quality results. Many cutting-edge generative AI models now use aggressive quantization combined with additional optimization techniques to maintain accuracy despite reduced precision.
Balancing Efficiency and Performance
While quantization dramatically improves efficiency, it does not automatically guarantee smooth performance on-device. The compressed model still needs to work with the device’s hardware and software layers, which have their own constraints. This is where runtime optimization comes in — another critical area of research at Samsung.
On-device AI runtimes must be designed to execute AI computations with minimal overhead. They need to intelligently schedule tasks, manage memory efficiently, and minimize unnecessary operations. They must also work closely with device hardware, from AI accelerators to specialized DSPs, to ensure that each operation runs on the most suitable processing unit.
Samsung’s researchers are optimizing these runtime environments to make the execution of quantized models even more efficient. By refining the way computations are scheduled and handled at the system level, the AI Center ensures that compressed models operate smoothly, even on devices with limited computing power.
Rethinking AI Architecture for On-Device Use
Compressing existing models is only one part of the equation. The next frontier in on-device AI involves designing entirely new AI architectures that are inherently lightweight and efficient.
The AI Center is developing models that strike the ideal balance between size and accuracy. These architectures are created with mobile constraints in mind from the very beginning, rather than being adapted from massive server-based models. This approach allows Samsung to develop AI that is not only smaller but also more coherent with the way users interact with their devices.
Future AI architectures may involve modular components that can be dynamically activated or deactivated, multi-resolution representations that adapt based on available resources, or hybrid models that combine local computation with selective cloud assistance when needed.
On-Device AI for a More Private and Responsive Future
One of the most compelling benefits of on-device AI is enhanced privacy. AI processing that occurs locally does not require sending sensitive data to a cloud server, giving users greater control and peace of mind. This is especially important for tasks involving personal images, voice commands, or private messages.
Local processing also delivers faster responses. By bypassing external servers, devices can generate AI-driven results with minimal latency — essential for real-time applications such as translation, smart camera features, and voice assistants.
Samsung believes that this combination of responsiveness and privacy is central to the next generation of user experiences, making on-device AI a key technology across its product portfolio.
A New Era of Ubiquitous Intelligence
The shift from cloud-only AI to hybrid and on-device AI marks a major turning point in how intelligent technology is integrated into everyday life. As Samsung continues to develop advanced optimization technologies — from quantization and compression to new runtime engines and model architectures — the performance gap between cloud AI and device AI is shrinking rapidly.
The vision is clear: devices that think faster, protect user privacy, and deliver seamless intelligent features without depending heavily on external infrastructure.
Source Link:https://news.samsung.com/
