i dont play fighting games and as for racing i am used to wasd thanks to vice city.

Oh yeah, the infamous core parking, I remember the Citra emulator struggling to run on my machine because the power plan was messed up, lol a machine who could run the latest titles, struggling with Citra of all things… anyway, I am aware Linux is just better, but I am too lazy to make a complete transition to this platform, got used with Windows for so long. I hope this DXVK can run on Windows because I just bought a AMD GPU, so who knows if old games will struggle. I intend to replay the Splinter Cell trilogy eventually, I am procrastinating… but I did installed Chaos Theory last year, running on the GTX 1060, had no issues (besides the controller not vibrating, which is distracting, I miss this feature from consoles… also, why the hell we don’t have mouses with vibration, it’s a cool little gimmick that is being around for ages)

Heh. Nice try. When I use a mkb it’s like I have cheats enabled. The hardest difficulty on any AAA game is a walk in the park.

I still use mkb on sweaty PvP shooters that I occasionally play these days because family and work get in the way.

You keep playing your sweaty shooters sitting in front of your 1080p 144hz.

Btw I’m curious to see how you do in racing, platformers and fighting games with your SUPERIOR input device 😅

I don’t even play racing and platformers to give you an idea. But if you insist, I drive around in gta 5 just fine with w a s d.

You suck with mouse & keyboard boomer. Only kiddypad lets you have some semblance of fun in your favourite walking simulators

LMao, another kid. Let me see you play racing, platformers or fighting games on your ‘superior’ device.

You just suck with controllers bruh.

LMao, another kid. Let me see you play racing, platformers or fighting games on your ‘superior’ device.

You just suck with controllers bruh.

Tens/Hundred thousands of people are wasting so much time for that, what trained networks can do in weeks/months. All time that could be used for tasks networks can’t do.

@@JohnDio:disqus
There is so much performance potential that will be unfolded in the next years till 2040 for accelerators, cpus, gpus, npus.
The upcoming Zen 5 (X3D) processored will look like amd’s ryzen 2700X processors from 6 years ago, once Zen 7 or 8 hits the market (Zen 6 will be the start of advanced packaging technologies). No joke. Look at that image (thank yourself for your silly censorship, anti-spam system here) 😉
pics(.)computerbase(.)de/1/1/0/2/8/3-e3bfa6ed98bcddea/14-2160.fee6eae8.png

Zen 5 is cheap 2D-package technologies from 5 years ago, only little by little advanced.
It’s pure milking by AMD right now to increase margins.
However they can’t stop progress forever.

It’s already 100 % given that Zen 7 or 8 (and thus RDNA 4 and 5) will use modern packaging technologies, 2.5D or 3D-hybrid stacking, as seen right now with the MI300X accelerators, or to some degree on Intel’s meteor lake.
The performance increase will be bigger than the incoming 30 – 35 % from Zen 4 -> Zen 5.
Step by step, the cpu-die will be placed directly above the I-/O-die and on top of that the cache.

It will be all vertical. All design will be optimized by machine learning to make use of the vertical axis.
That hasn’t happened at this point right now. Right now Zen 4 and Zen 5 will be simply “dumb” design, made for the old (current) “horizontal-era”.
The potential hasn’t been even unlocked.
The efficiency/performance gains will be massive.

There is no official confirmation of the amd employee working on “spatial-temporal upsampling with a form of TAUU, TAA” based on a machine learned trained network, but it’s obviously that they are. lol

After writing that long asz comments to you below – why FSR 2, DLSS 2/3 and XeSS should be called “image reconstruction” and not “upsampling/upscaling technologies” – I came to the same conclusion.

Machine learning (I don’t want to use that mainstream name AI) trained networks, are summarized “humanly impossible tasks made feasable”.
It’s exaflops of storage and trillions/quadtrillions lines of code.
Even if one company would gather 100k people and let them write such code in 5 years, it would be still humanly impossible to manage all that and put it together to make sense.

Thus AMD’s handcrafted approach will FSR 2 will never catch up in terms of performance and image quailty to DLSS 2/3, not even XeSS.

synopsys(.)com/ai/what-is-ai-chip-design.html
cadence(.)com/en_US/home/explore/ai-chip-design.html
arxiv(.)org/abs/2004.10746

The stupendous monotone human tasks takes years to design chips and it humanly impossible to make optimal.
The amount of possibilites is endless

The objective is to place
a netlist graph of macros (e.g., SRAMs) and standard cells
(logic gates, such as NAND, NOR, and XOR) onto a chip
canvas, such that power, performance, and area (PPA) are
optimized, while adhering to constraints on placement den-
sity and routing congestion

For those who don’t know: Nowadays silicon chips are basically quadtrillions kilometers of “lines and logic” and functions; that logic/functions are SRAM and all kind of gates. This is what computers see at the end, 0 & 1 and calculates them in through gates.
E.g. it takes 7 – 8 years to design a Zen 1/2/3/4/5/6/7/8 processor (yes, Amd employee already working on Zen 8 since 2021).