![]() Nvidia chose instead to imbue the SM with a dedicated datapath for integer operations. Bulking up the SM and Disconnecting the ROPįocussing on rasterisation first, you may recall that last-generation Turing had 64 FP32 cores, also known as Cuda cores, per SM, or exactly half that of the Pascal architecture that preceded it. Nvidia strives to improve the throughput and efficiency of each SM compared to its immediate predecessor whilst fitting more into the overall design by adopting a smaller manufacturing process. It's these SMs, like previous generations, where the bulk of the work is done. Zooming into the GA102 silicon reveals Nvidia houses 84 streaming multiprocessors (SMs) bunched into groups of 12 within a graphics processing cluster (GPC). Though RTX 3090 and RTX 3080 are known to use GA102, neither use its complete capability, leaving adequate room for a fully-enabled RTX Titan GPU down the line. The full GA102 die has 10,752 cores, 84 RT cores, 336 Tensor cores, more cache, more memory bandwidth, and PCIe 4.0 connectivity. ![]() Nvidia puts these transistors to work by cramming in more of just about everything that matters for a consumer GPU. The decision not to go with TSMC's 7nm is curious given Nvidia's long-standing relationship with the foundry, but Samsung's leading process has a similar transistor density as TSMC's popular 7nm, so the resultant GPUs would have been of similar size in any case. Known as GA102, it's a beast of a chip, comprising 28.3bn transistors on a 628.3mm² die manufactured on Samsung's 8nm custom process. Here is the usual high-level block diagram of the full gaming version of Ampere. It makes most sense to examine it in the fullest form and then focus on where Nvidia has made improvements over previous generations. So let's dig further into what Ampere is, how it's manifested in the RTX 3080, and then evaluate the validity of Nvidia's bombast performance claims.Īmpere is an evolution of the Turing architecture powering 20-series and 16-series cards. ![]() The launch is staggered such that the RTX 3080 is available from today, the champion RTX 3090 on September 24, and the RTX 3070 on October 15. GeForce 30-series is initially represented by the premium RTX 3070, RTX 3080, and RTX 3090. XeSS, being open-source and cross-platform is Raja Koduri’s secret weapon and from the looks of it, is going to replace NVIDIA’s DLSS soon enough.In a sign of the times, Nvidia boss, Jen-Hsun Huang, took the wraps off the all-new GeForce RTX 30-series graphics in none other than his kitchen.īased on the Ampere architecture that first debuted in the datacentre space with the A100 GPU, Nvidia reckons it has doubled the performance from the last generation. This goes on to show that Intel is focusing on the HPC and the AI segments first, and it won’t be surprising to see an entire lineup follow Ponte Vecchio for data centers. The same can be said of FP16 or half-precision/mixed-precision compute which is used to accelerate AI and neural networks, plus upscaling techniques such as XeSS. Double precision compute is largely redundant in gaming, but is important in the compute-intensive HPC space. It’s worth noting that most games solely rely on single-precision compute for the majority of their code, with a few newer games leveraging half-precision or FP16 for certain specialized workloads such as ray-tracing and upscaling. Looking at the figures, we can see that the fastest Intel Arc GPU falls behind the NVIDIA GeForce RTX 3070 Ti in single precision compute (FP32) but managed to be nearly twice as fast in double precision (FP64) workloads. Although not a very indicator of real-world gaming performance, it gives us an idea of Arc’s compute capabilities. An SiSoft benchmark of Intel’s upcoming Arc “Alchemist” flagship graphics card has surfaced.
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