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• NVIDIA is on a dumpster fire cheap “Samsung 8nm” process that is quite possibly the worst <=14nm process especially when it comes to power and heat. • Apple
by dannyw 6y ago
• NVIDIA is on a dumpster fire cheap “Samsung 8nm” process that is quite possibly the worst <=14nm process especially when it comes to power and heat.
• Apple has the benefits of complete vertical integration, both on the hardware and software side.
• Neural engine is essentially Tensor Cores in NVIDIA’s GPU but occupies at least 4x the equivalent die area as tensor cores (no public details on performance
yet).
• NVIDIA doesn’t want to make their consumer GPUs too powerful on tensor operations in order to not cannibalise their 1000% markup ML cards.
It’s almost a classic Intel: financial greed and financial engineering, combined with complacency from being long for so long.
Heck, AMD’s new top end card is tied with the 3090 - but $500 cheaper.
NVIDIA is reportedly scrambling to try and get back into TSMC who is going to make an example out of them.
- cromwellian 6y agoWait, you're comparing M1's neural engine with Ampere's tensorcores? Apple is talking TOPS, not TFLOPS, that means M1's 11 TOPs are for inference only, not training. By contrast, a tensorcore on Ampere does ~20 TFLOPS FP64, 78 TFLOPS FP16/BF16, and with GPU boost clock, up to 312 TFLOPs @ BF16. If you want to talk TOPS, an A100 does up to 1.2 exa-ops INT8, and 2.4 exa-tops INT4. That is, an A100 is more than 1000 times more powerful than the M1 at inferencing, while also supporting up to FP32/FP64 weights, and a 3080 is more than 500 times more powerful than the M1 neural engine.
- dannyw 6y agoAn A100 isn't a consumer GPU; a more valid comparison would be the RTX 3070.
- cromwellian 6y agoEven an RTC 2060 beats it, the 2060 has 52 TFLOPs worth of tensor core ops (not counting the CUDA cores), and it has >100 TOPs @ INT8, or 200+ TOPs @ INT4, so more than 10x the M1.