If you want a versatile and good value GPU with the knock out compute performance that punches well above its price tag, the EVGA RTX 2060 KO or KO ultra is the GPU for you.įor most graphic artists working with photographs or 2D design, a GPU provides a small but noticeable boost in performance particularly with Adobe suite products which can utilise CUDA cores to accelerate computationally intensive tasks like transforms. Of course, in gaming, it’s an RTX 2060 and will perform as such – which is to say it’s excellent at 1080p and more than capable of good 1440p gaming so long as you’re not intent on using the RTX features in the handful of games that offer them. This makes it a fantastic value purchase at $300-$320 if you intend on doing compute intensive tasks such as rendering in Blender – it’ll perform nearly as well as the RTX 2070 super in many cases. However it’s clear that not all of the die was deactivated, and in certain compute tasks the 2060 KO performs like the cards for which it’s processor was destined. These useable but incomplete chips are ‘fused off’ to reduce the render pipelines down to an RTX 2060 specification and have found their way into the RTX 2060 KO from EVGA. The reason was in the GPU die used: The RTX 20 Super cards use the TU 104 GPU die but some chips don’t make the cut in quality control validation. However, under test, these GPU’s showed a number of interesting performance discrepancies. In this article I’ll run through some of the specialised tasks GPU’s can excel in and make recommendations for professionals who want to perform processes on a desktop PC that until recently were the preserve of distributed computing networks.ĮVGA’s recently released revamp of the RTX 2060 was meant to be a simple price drop of the RTX 2060 to compete with the AMD RX 5600 XT launch. From things that make sense for 3D hardware, like rendering 3D scenes to esoteric uses including scientific simulation and analysing vast volumes of date hunting for patterns, GPU’s are coming to the fore in a wide range of practical applications. So long as a task is parallelisable, and can be written into a format that takes advantage of the strengths of a GPU, it’s possible to leverage the technology to perform a wide range of tasks far faster than CPU’s. Fundamentally GPU’s are massively parallel computers in their own right, with dedicated BIOS, specialised processing cores and RAM. GPU technology has been advancing relentlessly in recent years, primarily driven by the demands of gamers wanting the very best in graphical fidelity and framerates.
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