Single-core Performance, individual core benchmark performance, fX 8320.6. These library yield good speedups (3.6x-3.8x) and have predefined algorithms for parallelism on one machine across up to 4 GPUs. Fastest GPU
for a given budget. I rather run a few more experiments which are a bit slower than running just one experiment which is faster. The AMD A10-5800K APU hasn't been in the lead in any benchmark yet, but here we can see that it excels in graphics and had an overall score in Fire Strike of 1098. In short: GPUs are optimized for memory bandwidth while sacrificing for memory access time (latency). I personally have rather many small GPUs than one big one, even for my research experiments. Using Multiple GPUs Without Parallelism, another advantage of using multiple GPUs, even if you do not parallelize algorithms, is that you can run multiple algorithms or experiments separately on each GPU. AMD Radeon HD 7770 Mining GPU. GPU instances on Amazon web services are quite expensive and slow now and no longer pose a good option if you have less money. So making the right choice when it comes to buying a GPU is critical. It now again seems much more sensible to buy your own GPU. Based on shared data, this is the 12th overall best mining GPU and currently offers the best kH/ and 11th best kH/W ratio. Another important factor to consider however is that not all architectures are compatible with cuDNN. So how do you select the GPU which is right for you? We put the.5 GHz AMD FX 8320 to the test against the.5 GHz FX 6300 to find out which you should buy. So if you want to buy a fast GPU, first and foremost look at the bandwidth of that GPU.