Google Tensor G2 vs Apple A9X

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CPU comparison with benchmarks


Google Tensor G2 CPU1 vs CPU2 Apple A9X
Google Tensor G2 Apple A9X

CPU comparison

Google Tensor G2 or Apple A9X - 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 A9X has 2 cores with 2 threads and clocks with a maximum frequency of 2.26 GHz. The CPU supports up to 4 GB of memory in 2 memory channels. The Apple A9X was released in Q3/2015.
Google Tensor (3) Family Apple A series (22)
Google Tensor G2 (1) CPU group Apple A9/A9X (2)
2 Generation 9
G2 Architecture A9
Mobile Segment Mobile
Google Tensor Predecessor Apple A8X
-- Successor Apple A10X Fusion

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 A9X has 2 CPU cores and 2 threads can calculate simultaneously. The clock frequency of the Apple A9X is at 2.26 GHz.

Google Tensor G2 Characteristic Apple A9X
8 Cores 2
8 Threads 2
hybrid (Prime / big.LITTLE) Core architecture normal
No Hyperthreading No
No Overclocking ? No
2.85 GHz
2x Cortex-X1
A-Core 2.26 GHz
2x Twister
2.35 GHz
2x Cortex-A78
B-Core --
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 A9X
Google Tensor AI AI hardware --
Google Edge TPU @ 4 TOPS AI specifications --

Internal Graphics

The Google Tensor G2 or Apple A9X 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 A9X
0.90 GHz GPU frequency 0.65 GHz
-- GPU (Turbo) --
Vallhall 3 GPU Generation 6
4 nm Technology 16 nm
1 Max. displays 1
7 Compute units 48
-- Shader 384
No Hardware Raytracing No
No Frame Generation No
-- Max. GPU Memory 4 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 A9X
Decode / Encode Codec h265 / HEVC (8 bit) No
Decode / Encode Codec h265 / HEVC (10 bit) No
Decode / Encode Codec h264 Decode / Encode
Decode / Encode Codec VP9 No
Decode / Encode Codec VP8 Decode
Decode Codec AV1 No
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 A9X supports up to 4 GB of memory in 2 memory channels and achieves a memory bandwidth of up to 51.2 GB/s.

Google Tensor G2 Characteristic Apple A9X
LPDDR5-5500 Memory LPDDR4-3200
12 GB Max. Memory 4 GB
2 (Dual Channel) Memory channels 2 (Dual Channel)
53.0 GB/s Max. Bandwidth 51.2 GB/s
No ECC No
8.00 MB L2 Cache 3.00 MB
4.00 MB L3 Cache 4.00 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 A9X has a TDP of 8 W. The TDP specifies the necessary cooling solution that is required to cool the processor sufficiently.

Google Tensor G2 Characteristic Apple A9X
10 W TDP (PL1 / PBP) 8 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 A9X is manufactured in 16 nm and has a 7.00 MB cache.

Google Tensor G2 Characteristic Apple A9X
4 nm Technology 16 nm
Chiplet Chip design Chiplet
Armv8-A (64 bit) Instruction set (ISA) Armv8-A (64 bit)
-- ISA extensions --
-- Socket --
None Virtualization None
No AES-NI No
Android Operating systems iOS
Q4/2022 Release date Q3/2015
-- 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 4.0 stars (23 ratings). Rate now:
Here you can rate the Apple A9X to help other visitors make their purchasing decisions. The average rating is 0 stars (0 ratings). Rate now:


Average performance in benchmarks

⌀ Single core performance in 2 CPU benchmarks
Google Tensor G2 (100%)
Apple A9X (58%)
⌀ Multi core performance in 2 CPU benchmarks
Google Tensor G2 (100%)
Apple A9X (38%)

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 (100%)
Apple A9X Apple A9X
2C 2T @ 2.26 GHz
652 (61%)

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 (100%)
Apple A9X Apple A9X
2C 2T @ 2.26 GHz
1198 (38%)

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 (100%)
Apple A9X Apple A9X
2C 2T @ 2.26 GHz
763 (54%)

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 (100%)
Apple A9X Apple A9X
2C 2T @ 2.26 GHz
1251 (37%)

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 (100%)
Apple A9X Apple A9X
Apple A9X @ 0.65 GHz
499 (71%)

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 (100%)
Apple A9X Apple A9X
2C 2T @ 2.26 GHz
0 (0%)

AnTuTu 8 Benchmark

The AnTuTu 8 Benchmark measures the performance of a SoC. AnTuTu benchmarks the CPU, GPU, Memory as well as the UX (User Experience) by simulating browser and app usage. AnTuTu can benchmark any ARM CPU that runs under Android or iOS. Devices may not be directly compareable if the benchmark has been performed under different operating systems.

In the AnTuTu 8 benchmark, the single-core performance of a processor is only slightly weighted. The evaluation consists of the multi-core performance of the processor, the speed of the RAM and the performance of the internal graphics.
Google Tensor G2 Google Tensor G2
8C 8T @ 2.85 GHz
0 (0%)
Apple A9X Apple A9X
2C 2T @ 2.26 GHz
272103 (100%)

Estimated results for PassMark CPU Mark

Some of the CPUs listed below have been benchmarked by CPU-monkey. However the majority of CPUs have not been tested and the results have been estimated by a CPU-monkey’s secret proprietary formula. As such they do not accurately reflect the actual Passmark CPU mark values and are not endorsed by PassMark Software Pty Ltd.
Google Tensor G2 Google Tensor G2
8C 8T @ 2.85 GHz
0 (0%)
Apple A9X Apple A9X
2C 2T @ 2.26 GHz
2501 (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 (100%)
Apple A9X Apple A9X
2C 2T @ 2.26 GHz
0 (0%)

Devices using this processor

Google Tensor G2 Apple A9X
Google Pixel 7
Google Pixel 7 Pro
Apple iPad Pro (1. Gen)

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