Google Tensor G3 vs Apple A14 Bionic

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CPU comparison with benchmarks


Google Tensor G3 CPU1 vs CPU2 Apple A14 Bionic
Google Tensor G3 Apple A14 Bionic

CPU comparison

Google Tensor G3 or Apple A14 Bionic - 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 G3 has 8 cores with 8 threads and clocks with a maximum frequency of 2.91 GHz. Up to 12 GB of memory is supported in 2 memory channels. The Google Tensor G3 was released in Q3/2023.

The Apple A14 Bionic has 6 cores with 6 threads and clocks with a maximum frequency of 3.00 GHz. The CPU supports up to 6 GB of memory in 1 memory channels. The Apple A14 Bionic was released in Q3/2020.
Google Tensor (3) Family Apple A series (22)
Google Tensor G3 (1) CPU group Apple A14 (1)
3 Generation 14
G3 Architecture A14 (Firestorm/Icestorm)
Mobile Segment Mobile
Google Tensor Predecessor Apple A13 Bionic
-- Successor Apple A15 Bionic (5-GPU)

CPU Cores and Base Frequency

The Google Tensor G3 is a 8 core processor with a clock frequency of 2.91 GHz. The Apple A14 Bionic has 6 CPU cores with a clock frequency of 3.00 GHz.

Google Tensor G3 Characteristic Apple A14 Bionic
8 Cores 6
8 Threads 6
hybrid (Prime / big.LITTLE) Core architecture hybrid (big.LITTLE)
No Hyperthreading No
No Overclocking ? No
2.91 GHz
1x Cortex-X3
A-Core 3.00 GHz
2x Firestorm
2.37 GHz
4x Cortex-A715
B-Core 1.82 GHz
4x Icestorm
1.70 GHz
4x Cortex-A510
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 G3 Characteristic Apple A14 Bionic
Google Tensor AI AI hardware Apple Neural Engine
Google Edge TPU AI specifications 16 Neural cores @ 11 TOPS

Internal Graphics

The integrated graphics unit of a processor is not only responsible for the pure image output on the system, but can also significantly increase the efficiency of the system with the support of modern video codecs.

ARM Immortalis-G715 MP10 GPU Apple A14
0.89 GHz GPU frequency 1.46 GHz
-- GPU (Turbo) --
Vallhall GPU Generation 11
4 nm Technology 5 nm
0 Max. displays 3
10 Compute units 16
-- Shader 256
No Hardware Raytracing No
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 Immortalis-G715 MP10 GPU Apple A14
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 / Encode 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 G3 supports a maximum of 12 GB of memory in 2 memory channels. The Apple A14 Bionic can connect up to 6 GB of memory in 1 memory channels.

Google Tensor G3 Characteristic Apple A14 Bionic
LPDDR5-5500 Memory LPDDR4X-4266
12 GB Max. Memory 6 GB
2 (Dual Channel) Memory channels 1 (Single Channel)
53.0 GB/s Max. Bandwidth 34.1 GB/s
No ECC No
-- L2 Cache 12.00 MB
-- L3 Cache 16.00 MB
-- PCIe version --
-- PCIe lanes --
-- PCIe Bandwidth --

Thermal Management

The TDP (Thermal Design Power) of a processor specifies the required cooling solution. The Google Tensor G3 has a TDP of 10 W, that of the Apple A14 Bionic is 7.25 W.

Google Tensor G3 Characteristic Apple A14 Bionic
10 W TDP (PL1 / PBP) 7.25 W
-- TDP (PL2) --
-- TDP up --
-- TDP down --
-- Tjunction max. --

Technical details

The Google Tensor G3 has a 0.00 MB cache, while the Apple A14 Bionic cache has a total of 28.00 MB.

Google Tensor G3 Characteristic Apple A14 Bionic
4 nm Technology 5 nm
Chiplet Chip design Chiplet
Armv9-A (64 bit) Instruction set (ISA) Armv8-A (64 bit)
-- ISA extensions --
-- Socket --
None Virtualization None
No AES-NI No
Android Operating systems iOS
Q3/2023 Release date Q3/2020
-- Release price --
show more data show more data


Rate these processors

Here you can rate the Google Tensor G3 to help other visitors make their purchasing decisions. The average rating is 3.5 stars (18 ratings). Rate now:
Here you can rate the Apple A14 Bionic to help other visitors make their purchasing decisions. The average rating is 3.8 stars (36 ratings). Rate now:


Average performance in benchmarks

⌀ Single core performance in 2 CPU benchmarks
Google Tensor G3 (83%)
Apple A14 Bionic (100%)
⌀ Multi core performance in 3 CPU benchmarks
Google Tensor G3 (94%)
Apple A14 Bionic (94%)

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 G3 Google Tensor G3
8C 8T @ 2.91 GHz
1267 (80%)
Apple A14 Bionic Apple A14 Bionic
6C 6T @ 3.00 GHz
1590 (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 G3 Google Tensor G3
8C 8T @ 2.91 GHz
3631 (85%)
Apple A14 Bionic Apple A14 Bionic
6C 6T @ 3.00 GHz
4247 (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 G3 Google Tensor G3
8C 8T @ 2.91 GHz
1759 (85%)
Apple A14 Bionic Apple A14 Bionic
6C 6T @ 3.00 GHz
2072 (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 G3 Google Tensor G3
8C 8T @ 2.91 GHz
4533 (97%)
Apple A14 Bionic Apple A14 Bionic
6C 6T @ 3.00 GHz
4689 (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 G3 Google Tensor G3
ARM Immortalis-G715 MP10 @ 0.89 GHz
1 (0%)
Apple A14 Bionic Apple A14 Bionic
Apple A14 @ 1.46 GHz
749 (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 G3 Google Tensor G3
8C 8T @ 2.91 GHz
894587 (100%)
Apple A14 Bionic Apple A14 Bionic
6C 6T @ 3.00 GHz
729968 (82%)

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 G3 Google Tensor G3
8C 8T @ 2.91 GHz
1106280 (100%)
Apple A14 Bionic Apple A14 Bionic
6C 6T @ 3.00 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 G3 Google Tensor G3
8C 8T @ 2.91 GHz
0 (0%)
Apple A14 Bionic Apple A14 Bionic
6C 6T @ 3.00 GHz
628047 (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 G3 Google Tensor G3
8C 8T @ 2.91 GHz
0 (0%)
Apple A14 Bionic Apple A14 Bionic
6C 6T @ 3.00 GHz
8514 (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 G3 Google Tensor G3
8C 8T @ 2.91 GHz
0 (0%)
Apple A14 Bionic Apple A14 Bionic
6C 6T @ 3.00 GHz
11 (100%)

Devices using this processor

Google Tensor G3 Apple A14 Bionic
Google Pixel 8
Google Pixel 8 Pro
Apple iPhone 12 mini
Apple iPhone 12
Apple iPhone 12 Pro
Apple iPhone 12 Pro Max
Apple iPad Air (4. Gen)

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