KFA2 GeForce RTX 2070 EX OC in Crypto-Mining Ravencoin Hashrate (MH/s)

Here you can see how fast the KFA2 GeForce RTX 2070 EX OC is in Crypto-Mining Ravencoin Hashrate (MH/s). The performance of the graphics card in benchmarks or games primarily depends on the GPU architecture, the number of texture shaders and the memory size. The KFA2 GeForce RTX 2070 EX OC has 8 GB GDDR6 memory.
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GPU

Based on: NVIDIA GeForce RTX 2070
GPU Chip: TU106 (Turing)
Streaming Multiprocessors: 36
Shader: 2304
Raytracing Cores: 36

Memory

Memory Size: 8 GB
Memory Type: GDDR6
Memory Clock: 1.750 GHz
Memory bandwidth: 448 GB/s
Memory Interface: 256 bit

Benchmark results

Crypto-Mining Ravencoin Hashrate (MH/s)

KawPoW is a Proof-of-Work (PoW) algorithm and is calculated fastest with a modern graphics card. The hash rate values were determined under Hive OS with moderate overclocking.

KawPow Hashrate

KFA2 GeForce RTX 2070 EX OC KFA2 GeForce RTX 2070 EX OC
8 GB GDDR6
24 MH/s
NVIDIA GeForce RTX 2070 NVIDIA GeForce RTX 2070
Average of gpu group
22 MH/s

KawPow Power consumption

KFA2 GeForce RTX 2070 EX OC KFA2 GeForce RTX 2070 EX OC
8 GB GDDR6
196 W
NVIDIA GeForce RTX 2070 NVIDIA GeForce RTX 2070
Average of gpu group
175 W

KawPow Efficiency

KFA2 GeForce RTX 2070 EX OC KFA2 GeForce RTX 2070 EX OC
8 GB GDDR6
0.13 MH/W
NVIDIA GeForce RTX 2070 NVIDIA GeForce RTX 2070
Average of gpu group
0.13 MH/W


More benchmarks for the graphics card
KFA2 GeForce RTX 2070 EX OC

In order to determine the performance of a graphics card, so-called "benchmarks" are carried out. The benchmark software carries out special calculations to determine the performance of a graphics card. We use so-called theoretical or synthetic benchmarks (e.g. 3D Mark) as well as real game benchmarks. To ensure real comparability of the results, we pay attention to the correct execution of the benchmarks as well as the condition of the graphics card and the system.

We use the following benchmarks to measure the performance of a graphics card:


GPUs by group

Here we have listed more GPU groups for you:



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