Google Tensor vs Apple M1 (7-GPU)

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CPU comparison with benchmarks


Google Tensor CPU1 vs CPU2 Apple M1 (7-GPU)
Google Tensor Apple M1 (7-GPU)

CPU comparison

Google Tensor or Apple M1 (7-GPU) - 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 has 8 cores with 8 threads and clocks with a maximum frequency of 2.80 GHz. Up to 12 GB of memory is supported in 2 memory channels. The Google Tensor was released in Q4/2021.

The Apple M1 (7-GPU) has 8 cores with 8 threads and clocks with a maximum frequency of 3.20 GHz. The CPU supports up to 16 GB of memory in 2 memory channels. The Apple M1 (7-GPU) was released in Q4/2020.
Google Tensor (3) Family Apple M series (25)
Google Tensor (1) CPU group Apple M1 (9)
1 Generation 1
G1 Architecture M1
Mobile Segment Mobile
-- Predecessor --
Google Tensor G2 Successor --

CPU Cores and Base Frequency

The Google Tensor has 8 CPU cores and can calculate 8 threads in parallel. The clock frequency of the Google Tensor is 2.80 GHz while the Apple M1 (7-GPU) has 8 CPU cores and 8 threads can calculate simultaneously. The clock frequency of the Apple M1 (7-GPU) is at 0.60 GHz (3.20 GHz).

Google Tensor Characteristic Apple M1 (7-GPU)
8 Cores 8
8 Threads 8
hybrid (Prime / big.LITTLE) Core architecture hybrid (big.LITTLE)
No Hyperthreading No
No Overclocking ? No
2.80 GHz
2x Cortex-X1
A-Core 0.60 GHz (3.20 GHz)
4x Firestorm
2.25 GHz
2x Cortex-A76
B-Core 0.60 GHz (2.06 GHz)
4x Icestorm
1.80 GHz
4x Cortex-A55
C-Core --

NPU AI performance

The performance values of the processor's AI unit. The isolated NPU performance is specified here, the total AI performance (NPU+CPU+iGPU) can be higher. Processors with support for artificial intelligence (AI) and machine learning (ML) can process many calculations, especially audio, image and video processing, much faster than classic processors.

Google Tensor Characteristic Apple M1 (7-GPU)
Google Tensor AI AI hardware Apple Neural Engine
Google Edge TPU @ 1.6 TOPS AI specifications 16 Neural cores @ 11 TOPS
-- NPU + CPU + iGPU --

Integrated graphics (iGPU)

The Google Tensor or Apple M1 (7-GPU) 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-G78 MP20 GPU Apple M1 (7 Core)
0.76 GHz GPU frequency 0.39 GHz
-- GPU (Turbo) 1.30 GHz
Vallhall 2 GPU Generation 1
5 nm Technology 5 nm
1 Max. displays 2
20 Compute units 112
320 Shader 896
No Hardware Raytracing No
No Frame Generation No
-- Max. GPU Memory 8 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-G78 MP20 GPU Apple M1 (7 Core)
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
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 can use up to 12 GB of memory in 2 memory channels. The maximum memory bandwidth is 53.0 GB/s. The Apple M1 (7-GPU) supports up to 16 GB of memory in 2 memory channels and achieves a memory bandwidth of up to 68.2 GB/s.

Google Tensor Characteristic Apple M1 (7-GPU)
LPDDR5-5500 Memory LPDDR4X-4266
12 GB Max. Memory 16 GB
2 (Dual Channel) Memory channels 2 (Dual Channel)
53.0 GB/s Max. Bandwidth 68.2 GB/s
No ECC No
8.00 MB L2 Cache 16.00 MB
-- L3 Cache --
-- PCIe version 4.0
-- PCIe lanes --
-- PCIe Bandwidth --

Thermal Management

The thermal design power (TDP for short) of the Google Tensor is 10 W, while the Apple M1 (7-GPU) has a TDP of 18 W. The TDP specifies the necessary cooling solution that is required to cool the processor sufficiently.

Google Tensor Characteristic Apple M1 (7-GPU)
10 W TDP (PL1 / PBP) 18 W
-- TDP (PL2) --
-- TDP up 25 W
-- TDP down 10 W
-- Tjunction max. --

Technical details

The Google Tensor is manufactured in 5 nm and has 8.00 MB cache. The Apple M1 (7-GPU) is manufactured in 5 nm and has a 16.00 MB cache.

Google Tensor Characteristic Apple M1 (7-GPU)
5 nm Technology 5 nm
Unknown Chip design Chiplet
Armv8-A (64 bit) Instruction set (ISA) Armv8.5-A (64 bit)
-- ISA extensions Rosetta 2 x86-Emulation
-- Socket --
None Virtualization Apple Virtualization Framework
No AES-NI Yes
Android Operating systems macOS
Q4/2021 Release date Q4/2020
-- Release price --
show more data show more data


Rate these processors

Here you can rate the Google Tensor to help other visitors make their purchasing decisions. The average rating is 4.3 stars (14 ratings). Rate now:
Here you can rate the Apple M1 (7-GPU) to help other visitors make their purchasing decisions. The average rating is 4.9 stars (50 ratings). Rate now:


Average performance in benchmarks

⌀ Single core performance in 2 CPU benchmarks
Google Tensor (62%)
Apple M1 (7-GPU) (100%)
⌀ Multi core performance in 2 CPU benchmarks
Google Tensor (40%)
Apple M1 (7-GPU) (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 Google Tensor
8C 8T @ 2.80 GHz
1043 (60%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 3.20 GHz
1742 (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 Google Tensor
8C 8T @ 2.80 GHz
2915 (38%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 3.20 GHz
7650 (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 Google Tensor
8C 8T @ 2.80 GHz
1494 (63%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 3.20 GHz
2369 (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 Google Tensor
8C 8T @ 2.80 GHz
3639 (42%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 3.20 GHz
8576 (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 Google Tensor
ARM Mali-G78 MP20 @ 0.76 GHz
1943 (85%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
Apple M1 (7 Core) @ 1.30 GHz
2290 (100%)

AI performance (NPU)

The performance values of the processor's AI unit. The isolated NPU performance is given here, the total AI performance (NPU+CPU+iGPU) can be higher.

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 Google Tensor
8C 8T @ 2.80 GHz
1.6 (15%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 0.60 GHz
11 (100%)

Cinebench 2024 (Single-Core)

The Cinebench 2024 benchmark is based on the Redshift rendering engine, which is also used in Maxon's 3D program Cinema 4D. The benchmark runs are each 10 minutes long to test whether the processor is limited by its heat generation.
Google Tensor Google Tensor
8C 8T @ 2.80 GHz
0 (0%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 3.20 GHz
112 (100%)

Cinebench 2024 (Multi-Core)

The Multi-Core test of the Cinebench 2024 benchmark uses all cpu cores to render using the Redshift rendering engine, which is also used in Maxons Cinema 4D. The benchmark run is 10 minutes long to test whether the processor is limited by its heat generation.
Google Tensor Google Tensor
8C 8T @ 2.80 GHz
0 (0%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 3.20 GHz
509 (100%)

Cinebench R23 (Single-Core)

Cinebench R23 is the successor of Cinebench R20 and is also based on the Cinema 4 Suite. Cinema 4 is a worldwide used software to create 3D forms. The single-core test only uses one CPU core, the amount of cores or hyperthreading ability doesn't count.
Google Tensor Google Tensor
8C 8T @ 2.80 GHz
0 (0%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 3.20 GHz
1503 (100%)

Cinebench R23 (Multi-Core)

Cinebench R23 is the successor of Cinebench R20 and is also based on the Cinema 4 Suite. Cinema 4 is a worldwide used software to create 3D forms. The multi-core test involves all CPU cores and taks a big advantage of hyperthreading.
Google Tensor Google Tensor
8C 8T @ 2.80 GHz
0 (0%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 3.20 GHz
7759 (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 Google Tensor
8C 8T @ 2.80 GHz
691770 (100%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 0.60 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 Google Tensor
8C 8T @ 2.80 GHz
612494 (100%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 0.60 GHz
0 (0%)

Blender 3.1 Benchmark

In the Blender Benchmark 3.1, the scenes "monster", "junkshop" and "classroom" are rendered and the time required by the system is measured. In our benchmark we test the CPU and not the graphics card. Blender 3.1 was presented as a standalone version in March 2022.
Google Tensor Google Tensor
8C 8T @ 2.80 GHz
0 (0%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 3.20 GHz
117 (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 Google Tensor
8C 8T @ 2.80 GHz
0 (0%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 3.20 GHz
14463 (100%)

Blender 2.81 (bmw27)

Blender is a free 3D graphics software for rendering (creating) 3D bodies, which can also be textured and animated in the software. The Blender benchmark creates predefined scenes and measures the time (s) required for the entire scene. The shorter the time required, the better. We selected bmw27 as the benchmark scene.
Google Tensor Google Tensor
8C 8T @ 2.80 GHz
0 (0%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
8C 8T @ 3.20 GHz
314 (100%)

CPU performance per watt (efficiency)

Efficiency of the processor under full load in the Cinebench R23 (multi-core) benchmark. The benchmark result is divided by the average energy required (CPU package power in watts). The higher the value, the more efficient the CPU is under full load.
Google Tensor Google Tensor
2.80 GHz
0 (0%)
Apple M1 (7-GPU) Apple M1 (7-GPU)
7,759 CB R23 MC @ 18 W
431 (100%)

Devices using this processor

Google Tensor Apple M1 (7-GPU)
Google Pixel 6
Google Pixel 6 Pro
Unknown

News and articles for the Google Tensor and the Apple M1 (7-GPU)


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