Apple's M3 Ultra SoC is a massive ARM chip that packs a powerful 32-core CPU and an 80-core GPU, allowing for performance that trades blows with high-end workstations. If the early GPU benchmark scores are anything to go by, it sure does seem that the M3 Ultra is ready to take on RDNA 4 and Nvidia RTX 50 GPUs in most if not all workloads. https://www.notebookcheck.net/Apple-M3-Ultra-crushes-Nvidia-GeForce-RTX-5070-Ti-in-GPU-benchmark-but-falls-short-of-RTX-5080.977089.0.html
Yeah, the WORLD has been down this road before (the M4 series entire line results... just lies from shills, ect. when it comes to inference workloads, ect.) with the TRUE REAL-TIME RESULTS to these weak SoCs staged performance.
NO MOBILE CHIP will ever... EVER... compete with its rival flashship, or mid-tier, desktop GPU (heck, the M series line IS NOT even comparable to a dedicated flagship mobile GPU in REAL-TIME results); unless dedicated GPUs vendors (really, just NVIDIA) stop producing dGPUs.
Either show REAL-TIME PERFORMANCE... resolution setting, Ai model used, video codec used, duration of video revealed, the avg. FPS... on & on... ...or, it NEVER happened! 😏
Let's put this in perspective a bit, shall we?
Apple M3 Ultra: has 184 BILLION transistors at 3 nm.
The RTX 5070 Ti has 45.6 Billion Transistors at 5 nm.
184/45.6 = 4. The M3 has 4 times the amount of transistors.
And up to 512 GB of unified memory, NOT VRAM. VRAM is dedicated video RAM. Unified is for both system and video.
So yeah, an SoC that has 4 times the amount of transistors on a more advanced node, will do better.
Now, scale down that 184 to 45.6, roll it back to 5 nm and retest. How does it fair now?
Quote from: MigitMD on Today at 17:59:01Unified is for both system and video.
Subject to limitations (ca. 25% needs to stay for the system) and assignments (one can choose how much to use for either purpose).
Quotean SoC that has 4 times the amount of transistors on a more advanced node, will do better.
While your analysis has some value, your conclusion is wrong because the "software stack" (drivers, libraries and softwares) and the requirement of every particular software for RAM or VRAM (or unified memory assigned as eiher) also have a very great impact. Hardware expense is another aspect (M3 Ultra 512GB unified memory is all fine and well until you realise it is €10,000 and 4*RTX5090 might be an alternative if distributed 128GB VRAM should be enough).
If software is available / optimised for only one system, it will not / only badly work on other systems. If VRAM limit is essential for a software, it will only run on systems with enough VRAM (or assigned unified memory). Otherwise, software might be designed for both systems. While big LLMs might prefer large unified memory, most other AIs prefer Nvidia GPUs and libraries. There have been several examples for which choosing the right system means dozens of times greater speed. Also in the Nvidia - AMD - comparison.
Never just believe hardware numbers but always inform yourself on which system your preferred software will run at all or faster before buying hardware!