Google Tensor G2 vs Apple M2 Max (30-GPU)

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CPU comparison with benchmarks


Google Tensor G2 CPU1 vs CPU2 Apple M2 Max (30-GPU)
Google Tensor G2 Apple M2 Max (30-GPU)

CPU comparison

Google Tensor G2 or Apple M2 Max (30-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 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 M2 Max (30-GPU) has 12 cores with 12 threads and clocks with a maximum frequency of 3.50 GHz. The CPU supports up to 96 GB of memory in 4 memory channels. The Apple M2 Max (30-GPU) was released in Q1/2023.
Google Tensor (3) Family Apple M series (25)
Google Tensor G2 (1) CPU group Apple M2 (8)
2 Generation 2
G2 Architecture M2
Mobile Segment Mobile
Google Tensor Predecessor Apple M1 Max (24-GPU)
-- Successor Apple M3 Max (14-CPU 30-GPU)

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 M2 Max (30-GPU) has 12 CPU cores and 12 threads can calculate simultaneously. The clock frequency of the Apple M2 Max (30-GPU) is at 0.66 GHz (3.50 GHz).

Google Tensor G2 Characteristic Apple M2 Max (30-GPU)
8 Cores 12
8 Threads 12
hybrid (Prime / big.LITTLE) Core architecture hybrid (big.LITTLE)
No Hyperthreading No
No Overclocking ? No
2.85 GHz
2x Cortex-X1
A-Core 0.66 GHz (3.50 GHz)
8x Avalanche
2.35 GHz
2x Cortex-A78
B-Core 0.60 GHz (2.42 GHz)
4x Blizzard
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 M2 Max (30-GPU)
Google Tensor AI AI hardware Apple Neural Engine
Google Edge TPU @ 4 TOPS AI specifications 16 Neural cores @ 15.8 TOPS

Internal Graphics

The Google Tensor G2 or Apple M2 Max (30-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-G710 MP7 GPU Apple M2 Max (30 Core)
0.90 GHz GPU frequency 0.45 GHz
-- GPU (Turbo) 1.40 GHz
Vallhall 3 GPU Generation 2
4 nm Technology 5 nm
1 Max. displays 5
7 Compute units 480
-- Shader 3840
No Hardware Raytracing No
No Frame Generation No
-- Max. GPU Memory 96 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 M2 Max (30 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 G2 can use up to 12 GB of memory in 2 memory channels. The maximum memory bandwidth is 53.0 GB/s. The Apple M2 Max (30-GPU) supports up to 96 GB of memory in 4 memory channels and achieves a memory bandwidth of up to 409.6 GB/s.

Google Tensor G2 Characteristic Apple M2 Max (30-GPU)
LPDDR5-5500 Memory LPDDR5-6400
12 GB Max. Memory 96 GB
2 (Dual Channel) Memory channels 4 (Quad Channel)
53.0 GB/s Max. Bandwidth 409.6 GB/s
No ECC No
8.00 MB L2 Cache 36.00 MB
4.00 MB L3 Cache --
-- PCIe version 4.0
-- PCIe lanes 32
-- PCIe Bandwidth 63.0 GB/s

Thermal Management

The thermal design power (TDP for short) of the Google Tensor G2 is 10 W, while the Apple M2 Max (30-GPU) has a TDP of 45 W. The TDP specifies the necessary cooling solution that is required to cool the processor sufficiently.

Google Tensor G2 Characteristic Apple M2 Max (30-GPU)
10 W TDP (PL1 / PBP) 45 W
-- TDP (PL2) --
-- TDP up --
-- TDP down --
-- Tjunction max. 100 °C

Technical details

The Google Tensor G2 is manufactured in 4 nm and has 12.00 MB cache. The Apple M2 Max (30-GPU) is manufactured in 5 nm and has a 36.00 MB cache.

Google Tensor G2 Characteristic Apple M2 Max (30-GPU)
4 nm Technology 5 nm
Chiplet 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, iPadOS
Q4/2022 Release date Q1/2023
-- 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 4.2 stars (17 ratings). Rate now:
Here you can rate the Apple M2 Max (30-GPU) to help other visitors make their purchasing decisions. The average rating is 4.9 stars (14 ratings). Rate now:


Average performance in benchmarks

⌀ Single core performance in 2 CPU benchmarks
Google Tensor G2 (55%)
Apple M2 Max (30-GPU) (100%)
⌀ Multi core performance in 2 CPU benchmarks
Google Tensor G2 (22%)
Apple M2 Max (30-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 G2 Google Tensor G2
8C 8T @ 2.85 GHz
1068 (57%)
Apple M2 Max (30-GPU) Apple M2 Max (30-GPU)
12C 12T @ 3.50 GHz
1874 (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 (20%)
Apple M2 Max (30-GPU) Apple M2 Max (30-GPU)
12C 12T @ 3.50 GHz
15506 (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 (53%)
Apple M2 Max (30-GPU) Apple M2 Max (30-GPU)
12C 12T @ 3.50 GHz
2689 (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 (24%)
Apple M2 Max (30-GPU) Apple M2 Max (30-GPU)
12C 12T @ 3.50 GHz
14207 (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 (7%)
Apple M2 Max (30-GPU) Apple M2 Max (30-GPU)
Apple M2 Max (30 Core) @ 1.40 GHz
10650 (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 (25%)
Apple M2 Max (30-GPU) Apple M2 Max (30-GPU)
12C 12T @ 0.66 GHz
15.8 (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 G2 Google Tensor G2
8C 8T @ 2.85 GHz
0 (0%)
Apple M2 Max (30-GPU) Apple M2 Max (30-GPU)
12C 12T @ 3.50 GHz
121 (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 G2 Google Tensor G2
8C 8T @ 2.85 GHz
0 (0%)
Apple M2 Max (30-GPU) Apple M2 Max (30-GPU)
12C 12T @ 3.50 GHz
1025 (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 G2 Google Tensor G2
8C 8T @ 2.85 GHz
0 (0%)
Apple M2 Max (30-GPU) Apple M2 Max (30-GPU)
12C 12T @ 3.50 GHz
1695 (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 G2 Google Tensor G2
8C 8T @ 2.85 GHz
0 (0%)
Apple M2 Max (30-GPU) Apple M2 Max (30-GPU)
12C 12T @ 3.50 GHz
14855 (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 (100%)
Apple M2 Max (30-GPU) Apple M2 Max (30-GPU)
12C 12T @ 0.66 GHz
0 (0%)

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 M2 Max (30-GPU) Apple M2 Max (30-GPU)
12C 12T @ 3.50 GHz
26310 (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 G2 Google Tensor G2
2.85 GHz
0 (0%)
Apple M2 Max (30-GPU) Apple M2 Max (30-GPU)
14,855 CB R23 MC @ 40 W
371 (100%)

Devices using this processor

Google Tensor G2 Apple M2 Max (30-GPU)
Google Pixel 7
Google Pixel 7 Pro
Apple MacBook Pro 14 (2023)
Apple MacBook Pro 16 (2023)

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