this post was submitted on 23 Nov 2023
1 points (100.0% liked)
Hardware
47 readers
1 users here now
A place for quality hardware news, reviews, and intelligent discussion.
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
the 4070 ti at 294mm2 (full ad104) with 160 Tflops of Fp16
The 7900xtx GCD is 300 mm2 (Full Navi31 GCD only) with 122 tflops of Fp16
Doubt its that.
Where there might be reasons is that RDNA doesnt hasve AI cores. The tasks are accelerated on the shader cores.Hence the term AI Accelarators. Now assumming nvidia cards ignore the tensor cores.
The 4090 can do only 82.6 Tflop of FP16 (Non-Tensor).
The 7900xtx would still retain its 122 tflops of FP16. making it faster in Fp16 performance.
The actual rule has hard numbers, no need to speculate. And it's no more than 300 TFLOPS of fp16 (or 150 fp32, 600 fp8, etc) so it ain't TFLOPS that are the culprit. As for performance density, it's equivalent to those figures at an 830mm^2 die, so again not that.
Ok I didn’t know the actual numbers that’s helpful. Maybe they’re just holding off to apply for an export license? I heard the 4090 is in a “gray area”.
No gray area, at base clocks the 4090 exceeds the limit by 10% already.
doesn't RDNA3 have WAVA MMA or Wave Matrix Multiply Accumulate which is their AI cores?
It has the instruction sets in the compute units
They are called AI accelerators for that reason.
Not Ai cores.
The actual Matrix "Cores" , i.e. dedicated silicon, are on the instinct series
No. Tensor cores have seperate specialised matrix ALUs, AMD's WMMA are instructions on existing shader ALUs.
Tensor cores can process AI tasks in parallel to CUDA cores, RDNA3 can't do both on the same CU.