<P> This newer class of GPUs competes with integrated graphics in the low - end desktop and notebook markets . The most common implementations of this are ATI's HyperMemory and Nvidia's TurboCache . </P> <P> Hybrid graphics cards are somewhat more expensive than integrated graphics, but much less expensive than dedicated graphics cards . These share memory with the system and have a small dedicated memory cache, to make up for the high latency of the system RAM . Technologies within PCI Express can make this possible . While these solutions are sometimes advertised as having as much as 768MB of RAM, this refers to how much can be shared with the system memory . </P> <P> It is becoming increasingly common to use a general purpose graphics processing unit (GPGPU) as a modified form of stream processor (or a vector processor), running compute kernels . This concept turns the massive computational power of a modern graphics accelerator's shader pipeline into general - purpose computing power, as opposed to being hard wired solely to do graphical operations . In certain applications requiring massive vector operations, this can yield several orders of magnitude higher performance than a conventional CPU . The two largest discrete (see "Dedicated graphics cards" above) GPU designers, AMD and Nvidia, are beginning to pursue this approach with an array of applications . Both Nvidia and AMD have teamed with Stanford University to create a GPU - based client for the Folding@home distributed computing project, for protein folding calculations . In certain circumstances the GPU calculates forty times faster than the conventional CPUs traditionally used by such applications . </P> <P> GPGPU can be used for many types of embarrassingly parallel tasks including ray tracing . They are generally suited to high - throughput type computations that exhibit data - parallelism to exploit the wide vector width SIMD architecture of the GPU . </P>

The processing unit of most modern computers is a(n)