Leaked information about Nvidia N1X system-on-chip (SoC) has shown that the successor to the company will introduce a very powerful device to challenge its rivals in the AI workstation arena. Geekbench tests of engineering samples reveal the chip has 6,144 CUDA cores, spread across 48 streaming multiprocessors (SMs), the same number of cores with the desktop RTX 5070 GPU.
The N1X is the expectations of Nvidia to play in the ARM-based laptop processor segment and it has been designed with a complex architecture as an integration consisting of 20, latticework-based cerebral processor centers that are arranged into two groups of 10 each. Similar results can be seen in performance benchmarks, with the chip scoring 3,096 points in single-threaded and 18,837 points in multi-thread Geekbench testing that is competitive with AMD flagship 395 Ryzen AI Max+ mobile processor.
Important implications to AI Performance
The consideration of the RTX 5070-level GPU capabilities into a mobile SoC has significant consequences to AI-based loads. The N1X would be powerful enough to support more intensive machine learning due to the RTX 5070 having 23 percent improved performance on its predecessor and being a powerful AI acceleration platform. The trend mirrors the emergence of a huge industry opportunity around high-performance AI processing in mobile, as enterprises are coming to believe that they can use local inference of large language models and computer vision models on their devices.
Preliminary benchmarking already showed the N1X GPU working at conservative 1,048 MHz frequencies, indicating that the engineering samples still may have a lot to give in terms of performance. The CPU has been tested with a stunning amount of system RAM (128 GB), and a portion of this is devoted to GPU-related computing (64 GB), which demonstrates the intention of Nvidia to focus on memory-intensive software in the AI field.
The innovation puts Nvidia on the same level with Apple M-series processors and Qualcomm Snapdragon Elite in the high-end laptop market, and potentially ahead of the pack with acceleration of AI tasks due to its developed CUDA ecosystem and support of established machine learning frameworks.
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