NEW GOOGLE PIXEL 7 PRO BENCHMARK SHOWS SUPER POWERFUL CPU & GPU SPECS OF TENSOR G2

Google will soon launch the Pixel 7 series, using a completely new SoC developed and made by Google’s in-house engineers. With the latest SoC, called the Tensor G2, Google has made massive improvements in both the CPU and the GPU performance, making it one of the most powerful mobile SoCs to be released this year.

Android developer Kuba Wojciechowski has discovered what the Pixel 7 Pro and its Tensor G2 chip will bring to the table in terms of performance. Kuba took to Twitter to share a few screenshots of a new Geekbench listing for the Pixel 7 Pro.

The listing shows that the Pixel 7 Pro will likely feature 12GB of RAM, just like the Pixel 6 Pro. However, things get very interesting when we look at the processing cores and units.

Google has not changed much about the CPU cores used between last year’s Tensor and this year’s Tensor G2. The Tensor G2 uses the same combination of two Cortex-X1 cores, two Cortex-A76 cores, and four Cortex-A55 cores, albeit with slightly higher clock speeds.

Those slightly boosted speeds combined with the smaller 4nm die size appear to improve the Tensor G2 and Pixel 7 Pro multi-core benchmark by about 10 percent.

The Google Tensor SoC, or Tensor G1, was a powerful and capable processor when it was first launched with the Pixel 6. It wasn’t the most powerful, at least in terms of sheer benchmark scores, but it was still pretty impressive. The new generation improves things by significantly improving heat management and power efficiency.

The Tensor G2 SoC makes a massive jump in its GPU, which switches from the Mali-G78 to the Mali-G710. This new GPU is set to offer 20 percent better performance, 20 percent better power efficiency, and even 35 percent better for machine learning use, one of the keys focuses of the Tensor line.

Google also plans to add a new Tensor processing unit called the Janeiro in the Tensor G2. The Janeiro unit is supposed to improve the Tensor G2 SoC in neural network machine learning.

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