So apparently 580 8gig's are hard to come by now in the coin mining world. Since they like to buy 500 at a time I can believe that. And with the compute only cards coming out soon that offers some interesting things for the future (like a low power 470, or 470's, tha I can use as compute units in my mac pro :3 ).
For the moment, my 580 doesn't really do all that much. It games, renders video, thats about it. Image editing once in a while. So what can I make it do? I just watched the L1 machine learning intro video and it made me wonder what all I can make a GPU do for me.
So far I know I can do:
Coin Mining F@H Compute Networks (just in general) Machine Learning and Code Compile
I can do that crap. I would go to coin mining and go batshit crazy into Eth or Lite again, but the miners I try to use (CGMiner and BFGMiner) don't seem to see my GPU, nor do they really connect to the pools I use. So thats kinda womp.
1: Go fuck yourself 2: A kekaton 3: I'm a linux user. AMDGPU has a better driver line than any nvidia card does here. At that the 580 has better performance even in windows.
Selling it for a huge profit is actually a viable option right now if you can live without the 580 in your PC so noenkens suggestion is not a bad one and I don't think it was meant to insult you.
If you are trying to mine with it, make sure that you have drivers installed that support OpenCL and are using OpenCL based mining software. There are miners that are written for CUDA but they wont work on an AMD card
here are a selection of alternative things that you could use it for otherwise (from wikipedia):
The following are some of the areas where GPUs have been used for general purpose computing:
Computer clusters or a variant of a parallel computing (using GPU cluster technology) for highly calculation-intensive tasks: High-performance computing (HPC) clusters, often termed supercomputers including cluster technologies like Message Passing Interface, and single-system image (SSI), distributed computing, and Beowulf Grid computing (a form of distributed computing) (networking many heterogeneous computers to create a virtual computer architecture) Load-balancing clusters, sometimes termed a server farm Physical based simulation and physics engines (usually based on Newtonian physics models) Conway's Game of Life, cloth simulation, fluid incompressible flow by solution of Euler equations (fluid dynamics)[35] or Navier–Stokes equations[36] Statistical physics Ising model Lattice gauge theory Segmentation – 2D and 3D Level set methods CT reconstruction Fast Fourier transform GPU learning – machine learning and data mining computations, e.g., with software BIDMach k-nearest neighbor algorithm[37] Fuzzy logic[38] Tone mapping Audio signal processing Audio and sound effects processing, to use a GPU for digital signal processing (DSP) Analog signal processing Speech processing Digital image processing Video processing[39] Hardware accelerated video decoding and post-processing Motion compensation (mo comp) Inverse discrete cosine transform (iDCT) Variable-length decoding (VLD), Huffman coding Inverse quantization (IQ (not to be confused by Intelligence Quotient)) In-loop deblocking Bitstream processing (CAVLC/CABAC) using special purpose hardware for this task because this is a serial task not suitable for regular GPGPU computation Deinterlacing Spatial-temporal deinterlacing Noise reduction Edge enhancement Color correction Hardware accelerated video encoding and pre-processing Global illumination – ray tracing, photon mapping, radiosity among others, subsurface scattering Geometric computing – constructive solid geometry, distance fields, collision detection, transparency computation, shadow generation Scientific computing Monte Carlo simulation of light propagation[40] Weather forecasting Climate research Molecular modeling on GPU[41] Quantum mechanical physics Astrophysics[42] Bioinformatics[43][44] Computational finance Medical imaging Clinical decision support system (CDSS)[45] Computer vision Digital signal processing / signal processing Control engineering Operations research[46][47][48] Implementations of: the GPU Tabu Search algorithm solving the Resource Constrained Project Scheduling problem is freely available on GitHub;[49] the GPU algorithm solving the Nurse Rerostering problem is freely available on GitHub.[50] Neural networks Database operations[51][52][53] Lattice Boltzmann methods Cryptography and cryptanalysis Performance modeling: computationally intensive tasks on GPU[41] Implementations of: MD6, Advanced Encryption Standard (AES),[54][55] Data Encryption Standard (DES), RSA,[56] elliptic curve cryptography (ECC) Password cracking[57][58] Cryptocurrency transactions processing ("mining") (Bitcoin mining) Electronic design automation[59][60] Antivirus software[61][62] Intrusion detection[63][64]
I sold it for 50,- bucks over retail. I mean, yes I have a lot of stuff and did not need it but if you can survive for a wile without it, it is an even better idea for someone short on cash.
I mean I guess... I just don't have nice things that are up to date. I treasure what I can get. All my pentium 4 machines? I know how P4 ticks so they are stupid valuable to me. Probably not to anyone else but... Meh. Same thing with this 580. Wanted a new card that wasn't going to arbitrarily never get service so I went to the highest available. Its worked out so far.
Business is business. If you choose to sell it because you can profit from it, it is your decision and it is a perfectly fine decision. Remember that the value of anything is what someone is willing to pay for it. the buyer at $50 over MSRP means the card has that value to the buyer. Likewise, If that means keeping it and not taking the $50 in profit then that is part of the value you have for the card.
