The first is RTX-OPS, which takes into account general processing power as well as the GPU’s ray tracing and deep learning capabilities. Nvidia has also introduced two new metrics to help users differentiate between its new GPUs. This figure can be compared directly to previous generation Quadro products.
All GPUs are rated by FP32 performance, a measure of their single precision compute capabilities (TFLOPs) based on their CUDA cores alone. With this amount of memory Nvidia really can start to compete with the massively scaleable CPU architecture.Īs you can imagine, the higher up the range you go, the more cores you get (CUDA, tensor & RT). Those who can afford two Quadro RTX 8000s, for example, can effectively have a GPU with a colossal 96GB. This is done through NVlink, a proprietary Nvidia technology that is supported on the RTX 5000 and above. Users can also double the addressable memory by using two GPUs in the same workstation. It’s designed specifically to overcome the challenges of using the GPU for really high-end rendering where the datasets can be incredibly complex.
There’s also a new ultra high-end model, the Quadro RTX 8000 (48GB GDDR6), which is essentially the RTX 6000 with double the memory. The Quadro RTX 4000 (8GB GDDR6), RTX 5000 (16GB GDDR6) and RTX 6000 (24GB GDDR6) are essentially replacements for the Pascal-based Quadro P4000 (8GB GDDR5), P5000 (16GB GDDR5X) and P6000 (24GB GDDR5X). We imagine Nvidia will launch lower-end Quadro RTX GPUs later this year but we don’t know this for sure. Nvidia has launched four Quadro RTX GPUs, from the mid-range to the high-end. Then, when Quadro RTX-enabled software finally starts to ship, design viz folks will be able to generate photorealistic output even faster. So where does this leave us? At the moment, anyone investing in a Quadro RTX GPU can only use it in the same way they have used previous Quadro GPUs – just using its CUDA cores.īut the good news is, because the new Quadro RTX GPUs are a significant improvement over previous generations, this will be reason enough for many. And while there is widespread commitment from the industry, including Chaos Group (V-Ray), SolidWorks (Visualize) and Luxion (KeyShot) to name but a few, it will take time for commercial software to be released. In order for software to take advantage of these optimised cores, it has to be specifically written to do so. Nvidia Quadro RTX still has thousands of CUDA cores, so it can do all the things that Pascal and Maxwell could do (albeit faster) but it also features two additional sets of cores - RT Cores, which are optimised for ray tracing, and Tensor Cores, which are optimised for deep learning. Previous generation Quadro GPUs, such as Pascal (Quadro P2000, P4000 etc.) and Maxwell (Quadro M2000, M4000 etc.), featured thousands of general purpose Nvidia CUDA cores which could be used for 3D graphics or other parallel processing tasks such as ray trace rendering or simulation. Nvidia Quadro RTX is based on Nvidia’s new Turing architecture, which has been designed from the ground up for ray tracing and deep learning, a subset of Artificial Intelligence (AI). And these applications simply aren’t commercially available yet.īut before we get ahead of ourselves, it’s worth taking a step back to look at what makes Quadro RTX different to all GPUs that have come before. Quadro RTX might finally be shipping but as it is a completely new type of GPU technology, it also needs special software to take full advantage of its ray trace rendering capabilities. Nvidia had shown it could make photorealistic visualisation completely interactive and while there was almost certainly some smoke and mirrors, it was a massive advancement.įast forward six months and design viz artists can now start to see what all the fuss was about… well, kind of. Producing a single ray traced quality photoreal image used to take seconds or even minutes, but this was now being done in a fraction of a second on a desktop workstation, albeit one with two very powerful GPUs.