Google Tensor G2 vs Apple A17 Pro

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CPU comparison with benchmarks


Google Tensor G2 CPU1 vs CPU2 Apple A17 Pro
Google Tensor G2 Apple A17 Pro

CPU comparison

Google Tensor G2 or Apple A17 Pro - which processor is faster? In this comparison we look at the differences and analyze which of these two CPUs is better. We compare the technical data and benchmark results.

The Google Tensor G2 has 8 cores with 8 threads and clocks with a maximum frequency of 2.85 GHz. Up to 12 GB of memory is supported in 2 memory channels. The Google Tensor G2 was released in Q4/2022.

The Apple A17 Pro has 6 cores with 6 threads and clocks with a maximum frequency of 3.78 GHz. The CPU supports up to 8 GB of memory in 1 memory channels. The Apple A17 Pro was released in Q3/2023.
Google Tensor (3) Family Apple A series (22)
Google Tensor G2 (1) CPU group Apple A17 (1)
2 Generation 17
G2 Architecture A17
Mobile Segment Mobile
Google Tensor Predecessor Apple A16 Bionic
-- Successor --

CPU Cores and Base Frequency

The Google Tensor G2 has 8 CPU cores and can calculate 8 threads in parallel. The clock frequency of the Google Tensor G2 is 2.85 GHz while the Apple A17 Pro has 6 CPU cores and 6 threads can calculate simultaneously. The clock frequency of the Apple A17 Pro is at 3.78 GHz.

Google Tensor G2 Characteristic Apple A17 Pro
8 Cores 6
8 Threads 6
hybrid (Prime / big.LITTLE) Core architecture hybrid (big.LITTLE)
No Hyperthreading No
No Overclocking ? No
2.85 GHz
2x Cortex-X1
A-Core 3.78 GHz
2.35 GHz
2x Cortex-A78
B-Core 2.11 GHz
1.80 GHz
4x Cortex-A55
C-Core --

Artificial Intelligence and Machine Learning

Processors with the support of artificial intelligence (AI) and machine learning (ML) can process many calculations, especially audio, image and video processing, much faster than classic processors. Algorithms for ML improve their performance the more data they have collected via software. ML tasks can be processed up to 10,000 times faster than with a classic processor.

Google Tensor G2 Characteristic Apple A17 Pro
Google Tensor AI AI hardware Apple Neural Engine
Google Edge TPU @ 4 TOPS AI specifications 16 Neural cores @ 35 TOPS

Internal Graphics

The Google Tensor G2 or Apple A17 Pro has integrated graphics, called iGPU for short. The iGPU uses the system's main memory as graphics memory and sits on the processor's die.

ARM Mali-G710 MP7 GPU Apple A17 Pro (6 GPU Cores)
0.90 GHz GPU frequency 1.40 GHz
-- GPU (Turbo) --
Vallhall 3 GPU Generation --
4 nm Technology 3 nm
1 Max. displays 3
7 Compute units 24
-- Shader 768
No Hardware Raytracing Yes
No Frame Generation No
-- Max. GPU Memory 6 GB
12 DirectX Version --

Hardware codec support

A photo or video codec that is accelerated in hardware can greatly accelerate the working speed of a processor and extend the battery life of notebooks or smartphones when playing videos.

ARM Mali-G710 MP7 GPU Apple A17 Pro (6 GPU Cores)
Decode / Encode Codec h265 / HEVC (8 bit) Decode / Encode
Decode / Encode Codec h265 / HEVC (10 bit) Decode / Encode
Decode / Encode Codec h264 Decode / Encode
Decode / Encode Codec VP9 Decode / Encode
Decode / Encode Codec VP8 Decode / Encode
Decode Codec AV1 Decode
Decode / Encode Codec AVC Decode
Decode / Encode Codec VC-1 Decode
Decode / Encode Codec JPEG Decode / Encode

Memory & PCIe

The Google Tensor G2 can use up to 12 GB of memory in 2 memory channels. The maximum memory bandwidth is 53.0 GB/s. The Apple A17 Pro supports up to 8 GB of memory in 1 memory channels and achieves a memory bandwidth of up to 51.2 GB/s.

Google Tensor G2 Characteristic Apple A17 Pro
LPDDR5-5500 Memory LPDDR5-6400
12 GB Max. Memory 8 GB
2 (Dual Channel) Memory channels 1 (Single Channel)
53.0 GB/s Max. Bandwidth 51.2 GB/s
No ECC No
8.00 MB L2 Cache 20.00 MB
4.00 MB L3 Cache 24.00 MB
-- PCIe version --
-- PCIe lanes --
-- PCIe Bandwidth --

Thermal Management

The thermal design power (TDP for short) of the Google Tensor G2 is 10 W, while the Apple A17 Pro has a TDP of 11 W. The TDP specifies the necessary cooling solution that is required to cool the processor sufficiently.

Google Tensor G2 Characteristic Apple A17 Pro
10 W TDP (PL1 / PBP) 11 W
-- TDP (PL2) --
-- TDP up --
-- TDP down --
-- Tjunction max. --

Technical details

The Google Tensor G2 is manufactured in 4 nm and has 12.00 MB cache. The Apple A17 Pro is manufactured in 3 nm and has a 44.00 MB cache.

Google Tensor G2 Characteristic Apple A17 Pro
4 nm Technology 3 nm
Chiplet Chip design Chiplet
Armv8-A (64 bit) Instruction set (ISA) Armv8.6-A (64 bit)
-- ISA extensions --
-- Socket --
None Virtualization None
No AES-NI No
Android Operating systems iOS
Q4/2022 Release date Q3/2023
-- Release price --
show more data show more data


Rate these processors

Here you can rate the Google Tensor G2 to help other visitors make their purchasing decisions. The average rating is 3.9 stars (21 ratings). Rate now:
Here you can rate the Apple A17 Pro to help other visitors make their purchasing decisions. The average rating is 4.0 stars (70 ratings). Rate now:


Average performance in benchmarks

⌀ Single core performance in 2 CPU benchmarks
Google Tensor G2 (49%)
Apple A17 Pro (100%)
⌀ Multi core performance in 3 CPU benchmarks
Google Tensor G2 (57%)
Apple A17 Pro (100%)

Geekbench 5, 64bit (Single-Core)

Geekbench 5 is a cross plattform benchmark that heavily uses the systems memory. A fast memory will push the result a lot. The single-core test only uses one CPU core, the amount of cores or hyperthreading ability doesn't count.
Google Tensor G2 Google Tensor G2
8C 8T @ 2.85 GHz
1068 (50%)
Apple A17 Pro Apple A17 Pro
6C 6T @ 3.78 GHz
2140 (100%)

Geekbench 5, 64bit (Multi-Core)

Geekbench 5 is a cross plattform benchmark that heavily uses the systems memory. A fast memory will push the result a lot. The multi-core test involves all CPU cores and taks a big advantage of hyperthreading.
Google Tensor G2 Google Tensor G2
8C 8T @ 2.85 GHz
3149 (54%)
Apple A17 Pro Apple A17 Pro
6C 6T @ 3.78 GHz
5868 (100%)

Geekbench 6 (Single-Core)

Geekbench 6 is a benchmark for modern computers, notebooks and smartphones. What is new is an optimized utilization of newer CPU architectures, e.g. based on the big.LITTLE concept and combining CPU cores of different sizes. The single-core benchmark only evaluates the performance of the fastest CPU core, the number of CPU cores in a processor is irrelevant here.
Google Tensor G2 Google Tensor G2
8C 8T @ 2.85 GHz
1426 (48%)
Apple A17 Pro Apple A17 Pro
6C 6T @ 3.78 GHz
2952 (100%)

Geekbench 6 (Multi-Core)

Geekbench 6 is a benchmark for modern computers, notebooks and smartphones. What is new is an optimized utilization of newer CPU architectures, e.g. based on the big.LITTLE concept and combining CPU cores of different sizes. The multi-core benchmark evaluates the performance of all of the processor's CPU cores. Virtual thread improvements such as AMD SMT or Intel's Hyper-Threading have a positive impact on the benchmark result.
Google Tensor G2 Google Tensor G2
8C 8T @ 2.85 GHz
3342 (45%)
Apple A17 Pro Apple A17 Pro
6C 6T @ 3.78 GHz
7462 (100%)

iGPU - FP32 Performance (Single-precision GFLOPS)

The theoretical computing performance of the internal graphics unit of the processor with simple accuracy (32 bit) in GFLOPS. GFLOPS indicates how many billion floating point operations the iGPU can perform per second.
Google Tensor G2 Google Tensor G2
ARM Mali-G710 MP7 @ 0.90 GHz
700 (33%)
Apple A17 Pro Apple A17 Pro
Apple A17 Pro (6 GPU Cores) @ 1.40 GHz
2147 (100%)

AnTuTu 9 Benchmark

The AnTuTu 9 benchmark is very well suited to measuring the performance of a smartphone. AnTuTu 9 is quite heavy on 3D graphics and can now also use the "Metal" graphics interface. In AnTuTu, memory and UX (user experience) are also tested by simulating browser and app usage. AnTuTu version 9 can compare any ARM CPU running on Android or iOS. Devices may not be directly comparable when benchmarked on different operating systems.

In the AnTuTu 9 benchmark, the single-core performance of a processor is only slightly weighted. The rating is made up of the multi-core performance of the processor, the speed of the working memory, and the performance of the internal graphics.
Google Tensor G2 Google Tensor G2
8C 8T @ 2.85 GHz
789419 (71%)
Apple A17 Pro Apple A17 Pro
6C 6T @ 3.78 GHz
1108570 (100%)

Performance for Artificial Intelligence (AI) and Machine Learning (ML)

Processors with the support of artificial intelligence (AI) and machine learning (ML) can process many calculations, especially audio, image and video processing, much faster than classic processors. The performance is given in the number (trillions) of arithmetic operations per second (TOPS).
Google Tensor G2 Google Tensor G2
8C 8T @ 2.85 GHz
4 (11%)
Apple A17 Pro Apple A17 Pro
6C 6T @ 3.78 GHz
35 (100%)

AnTuTu 10 Benchmark

The AnTuTu 10 benchmark is one of the best-known benchmarks for mobile processors, which is now available in version 10. There is a version for Android-based smartphones and tablets, as well as a version for Apple mobile devices, i.e. iPhones and iPads.

The Antutu 10 benchmark has 3 phases. In the first phase, the device's RAM is tested, in phase 2 the graphics are tested and in the final phase the entire device is pushed to its performance limits by rendering 3D graphics.

Antutu 10 is therefore ideal for comparing the performance of different devices.
Google Tensor G2 Google Tensor G2
8C 8T @ 2.85 GHz
0 (0%)
Apple A17 Pro Apple A17 Pro
6C 6T @ 3.78 GHz
1488440 (100%)

Devices using this processor

Google Tensor G2 Apple A17 Pro
Google Pixel 7
Google Pixel 7 Pro
Apple iPhone 15 Pro
Apple iPhone 15 Pro Max

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