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Google Tensor vs Apple A13 Bionic

Google Tensor

The Google Tensor operates with 8 cores and 8 CPU threads. It run at 1.80 GHz (2.80 GHz) base -- all cores while the TDP is set at 10 W.The processor is attached to the N/A CPU socket. This version includes -- of L3 cache on one chip, supports 2 memory channels to support LPDDR5-5500 RAM and features PCIe Gen lanes. Tjunction keeps below -- degrees C. In particular, G1 Architecture is enhanced with 5 nm technology and supports None. The product was launched on Q4/2021

Google Tensor

The Apple A13 Bionic operates with 6 cores and 8 CPU threads. It run at 2.65 GHz base 1.80 GHz all cores while the TDP is set at 6 W.The processor is attached to the N/A CPU socket. This version includes -- of L3 cache on one chip, supports 1 memory channels to support LPDDR4X-4266 RAM and features PCIe Gen lanes. Tjunction keeps below -- degrees C. In particular, A13 (Lightning / Thunder) Architecture is enhanced with 7 nm technology and supports None. The product was launched on Q3/2019


Compare Detail

1.80 GHz (2.80 GHz) Frequency 2.65 GHz
8 Cores 6
1.80 GHz (2.80 GHz) Turbo (1 Core) 2.65 GHz
-- Turbo (All Cores) 1.80 GHz
uncheck No Hyperthreading No
uncheck No Overclocking No uncheck
hybrid (Prime / big.LITTLE) Core Architecture hybrid (big.LITTLE)
ARM Mali-G78 MP20 GPU Apple A13
No turbo GPU (Turbo) No turbo
5 nm Technology 7 nm
No turbo GPU (Turbo) No turbo
12 DirectX Version
1 Max. displays 1
LPDDR5-5500 Memory LPDDR4X-4266
2 Memory channels 1
Max memory
uncheck No ECC No uncheck
8.00 MB L2 Cache 8.00 MB
-- L3 Cache --
PCIe version
PCIe lanes
5 nm Technology 7 nm
N/A Socket N/A
10 W TDP 6 W
None Virtualization None
Q4/2021 Release date Q3/2019

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 1,049 (47%)
47% Complete
Apple A13 Bionic 1,344 (60%)
60% Complete

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 2,916 (6%)
6% Complete
Apple A13 Bionic 3,430 (7%)
7% Complete

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 1,944 (9%)
9% Complete
Apple A13 Bionic 692 (3%)
3% Complete

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.

0% Complete
Apple A13 Bionic 523,659 (73%)
73% Complete

Geekbench 3, 64bit (Single-Core)

Geekbench 3 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 4,572 (66%)
66% Complete
0% Complete

Geekbench 3, 64bit (Multi-Core)

Geekbench 3 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 12,597 (13%)
13% Complete
0% Complete
Electric Usage Estimate

Electric Usage Estimate

Electric Usage Estimate

Electric Usage Estimate

Google Tensor Apple A13 Bionic
10 W Max TDP 6 W
NA Power consumption per day (kWh) NA
NA Running cost per day NA
NA Power consumption per year (kWh) NA
NA Running cost per year NA

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