Samsung is taking another big step toward faster on-device AI with a new agreement that directly benefits its upcoming Exynos 2500 chipset. The deal focuses on making AI models run more efficiently on the device itself, reducing the need to rely on cloud computing for demanding generative AI tasks.
At the center of this move is a strategic partnership with Nota AI. Under the agreement, Nota AI will provide its AI model compression and optimization technology for the Exynos 2500. In practice, this means AI models can be made smaller, lighter, and better optimized so they run faster and consume fewer resources when operating on the phone or tablet—exactly what’s needed as on-device AI features continue to expand.
Samsung has already been using Nota AI’s technology as part of its Exynos AI Studio, an in-house AI model optimization toolchain. The goal is to help advanced AI models run efficiently by taking advantage of the Exynos 2500’s processing capabilities, while keeping more workloads on the device instead of sending them to the cloud. That can also translate into quicker responses, improved privacy for certain tasks, and more consistent performance when you don’t have a strong network connection.
Nota AI CEO Myungsu Chae described the collaboration as more than a simple software supply arrangement, emphasizing that the technology is built as an integrated framework where AI hardware and software are designed to work together to deliver high-performance generative AI at the edge.
For readers tracking the chip itself, the Exynos 2500 includes a mix of CPU, GPU, and dedicated AI hardware designed for modern flagship workloads. Its architecture features a 10-core CPU layout with one Cortex-X925 prime core clocked at 3.30GHz, two Cortex-A725 cores at 2.74GHz, five Cortex-A725 cores at 2.36GHz, and two Cortex-A520 efficiency cores at 1.80GHz. On the graphics side, it uses a Samsung Xclipse 950 GPU based on AMD’s RDNA architecture. AI tasks are supported by a dedicated Neural Processing Unit (NPU) rated at 59 TOPS, paired with memory support listed at 76.8 Gb/s LPDDR5X RAM.
While those numbers point to a capable on-device AI platform, it’s also worth noting that the Exynos 2500’s NPU output is below a competing mobile NPU reportedly reaching 100 TOPS. That gap helps explain why Samsung’s agreement with Nota AI matters: better model compression and optimization can significantly improve real-world AI performance, even when raw NPU throughput isn’t the highest on paper.
In other words, Samsung appears to be doubling down on smarter software and tighter hardware-software integration to ensure the Exynos 2500 handles AI workloads more efficiently. If the optimized toolchain delivers as intended, it could make on-device generative AI experiences smoother and more practical—without leaning as heavily on cloud processing.
