Samsung’s cutting-edge semiconductors are accelerating the AI revolution — delivering the performance, efficiency, and scalability required for everything from large-scale model training to real-time edge inference.
Samsung’s cutting-edge semiconductors are accelerating the AI revolution — delivering the performance, efficiency, and scalability required for everything from large-scale model training to real-time edge inference.
Samsung’s cutting-edge semiconductors are accelerating the AI revolution — delivering the performance, efficiency, and scalability required for everything from large-scale model training to real-time edge inference.
Artificial Intelligence (AI) workloads are rapidly expanding beyond centralized data centers to edge devices, enabling real-time processing in smartphones, smart cameras, industrial sensors, and more. This shift requires semiconductors that deliver high performance, low latency, and power efficiency across diverse form factors. These advancements bring intelligence closer to the data sources, improving responsiveness and reducing dependence on cloud infrastructure.
Artificial Intelligence (AI) workloads are rapidly expanding beyond centralized data centers to edge devices, enabling real-time processing in smartphones, smart cameras, industrial sensors, and more. This shift requires semiconductors that deliver high performance, low latency, and power efficiency across diverse form factors. These advancements bring intelligence closer to the data sources, improving responsiveness and reducing dependence on cloud infrastructure.
Artificial Intelligence (AI) workloads are rapidly expanding beyond centralized data centers to edge devices, enabling real-time processing in smartphones, smart cameras, industrial sensors, and more. This shift requires semiconductors that deliver high performance, low latency, and power efficiency across diverse form factors. These advancements bring intelligence closer to the data sources, improving responsiveness and reducing dependence on cloud infrastructure.
As AI models grow in size and complexity, the demand for faster and more efficient memory systems intensifies. Memory bandwidth and capacity have become critical performance bottlenecks in both training and inference phases. To address these challenges, cutting-edge solutions such as High Bandwidth Memory (HBM), Low Power Double Data Rate (LPDDR), Graphics Double Data Rate (GDDR), and PCIe Gen5 Solid State Drives (SSDs) are being leveraged to maximize throughput and minimize latency across AI applications.
As AI models grow in size and complexity, the demand for faster and more efficient memory systems intensifies. Memory bandwidth and capacity have become critical performance bottlenecks in both training and inference phases. To address these challenges, cutting-edge solutions such as High Bandwidth Memory (HBM), Low Power Double Data Rate (LPDDR), Graphics Double Data Rate (GDDR), and PCIe Gen5 Solid State Drives (SSDs) are being leveraged to maximize throughput and minimize latency across AI applications.
As AI models grow in size and complexity, the demand for faster and more efficient memory systems intensifies. Memory bandwidth and capacity have become critical performance bottlenecks in both training and inference phases. To address these challenges, cutting-edge solutions such as High Bandwidth Memory (HBM), Low Power Double Data Rate (LPDDR), Graphics Double Data Rate (GDDR), and PCIe Gen5 Solid State Drives (SSDs) are being leveraged to maximize throughput and minimize latency across AI applications.
Large-scale AI models, known as foundation models, are transforming industries by enabling a wide range of capabilities--from language translation to image generation. These models demand significant computational power, memory bandwidth, and storage throughput. Their development and deployment require advanced hardware solutions that can support intensive training and inference workloads across data centers and cloud platforms.
Large-scale AI models, known as foundation models, are transforming industries by enabling a wide range of capabilities--from language translation to image generation. These models demand significant computational power, memory bandwidth, and storage throughput. Their development and deployment require advanced hardware solutions that can support intensive training and inference workloads across data centers and cloud platforms.
Large-scale AI models, known as foundation models, are transforming industries by enabling a wide range of capabilities--from language translation to image generation. These models demand significant computational power, memory bandwidth, and storage throughput. Their development and deployment require advanced hardware solutions that can support intensive training and inference workloads across data centers and cloud platforms.
Balancing AI innovation with energy efficiency is becoming increasingly crucial as data volumes and model sizes continue to expand. Sustainable computing has emerged as a key priority for both data centers and on-device AI. Advancements in semiconductor process technologies, such as Extreme Ultraviolet (EUV) lithography and 3D packaging, are designed to reduce power consumption while preserving performance. When paired with low-power DRAM and high-efficiency Power Management Integrated Circuits (PMICs), these innovations help drive the development of more environmentally friendly AI systems.
Balancing AI innovation with energy efficiency is becoming increasingly crucial as data volumes and model sizes continue to expand. Sustainable computing has emerged as a key priority for both data centers and on-device AI. Advancements in semiconductor process technologies, such as Extreme Ultraviolet (EUV) lithography and 3D packaging, are designed to reduce power consumption while preserving performance. When paired with low-power DRAM and high-efficiency Power Management Integrated Circuits (PMICs), these innovations help drive the development of more environmentally friendly AI systems.
Balancing AI innovation with energy efficiency is becoming increasingly crucial as data volumes and model sizes continue to expand. Sustainable computing has emerged as a key priority for both data centers and on-device AI. Advancements in semiconductor process technologies, such as Extreme Ultraviolet (EUV) lithography and 3D packaging, are designed to reduce power consumption while preserving performance. When paired with low-power DRAM and high-efficiency Power Management Integrated Circuits (PMICs), these innovations help drive the development of more environmentally friendly AI systems.
