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I don't see how nvidia come out of this stronger their huge customers will be able to produce ASICs hat will be faster and cheaper to operate than their GPUs
by blibble 10mo ago
I don't see how nvidia come out of this stronger
their huge customers will be able to produce ASICs hat will be faster and cheaper to operate than their GPUs
jensen has to be the luckiest man in the world, first crypto, now "AI"
- malux85 10mo agoGaming, then crypto, then AI - all GPU hungry!
- flopsamjetsam 10mo agoAnd each one requiring an order of magnitude more GPUs than the last!
- bdangubic 10mo agothis trend will always continue with next big thing
- vb-8448 10mo agoNvidia is the Cisco of .com ... cisco still exists, and it's doing pretty well.
- cr125rider 10mo agoI forgot they bought Splunk. Enterprises love shoveling money into that fire pit
- diamond559 10mo agoJust took them a few 26 years to touch their peak dot com bubble stock price again.
- vb-8448 10mo agoAnd it will be probably the same for nvidia, unless they didn't find another business stream apart from selling "shovels". BTW, stock price is not everything, Cisco survived, grew, and it's the backbone of internet today.
- eastbound 10mo agoI wonder what’s going to be the “I can cloud-configure all my Cisco routers over the internet” for Nvidia.
- patapong 10mo ago> their huge customers will be able to produce ASICs hat will be faster and cheaper to operate than their GPUs Are we sure this will be the case? Perhaps the sweet spot for hardware that can train/run language models is the GPU already, especially with the years of head start Nvidia has?
- jonas21 10mo ago> their huge customers will be able to produce ASICs hat will be faster and cheaper to operate than their GPUs Why? NVIDIA is better positioned to produce faster and more efficient ML ASICs any of their huge customers (except possibly Google). And on top of that, the fact that there is a huge library of CUDA code that will run out of the box on NVIDIA hardware is a big advantage. Arguably, this shift has already happened. Modern NVIDIA datacenter GPUs, like the H100, only bear a passing resemblance to a GPU -- most of the silicon is dedicated to accelerating ML workloads.
- deleted 10mo ago[deleted]
- throw234234234 10mo agoI think this is what the "circular financing" is all about actually. While you are in the 'picks and shovels' phase you want to use your high margins to buy up the value chain and become more vertically integrated. Effectively investing when the sun shines to diversify the company. As a possibility for example I can see them transforming from a GPU based corp into a parent company for many full or partially owned "subsidiaries". They still manufacture chips to be "vertically integrated" but that becomes bread and butter as an enablement rather than the main story (e.g. Google TPU's). As their margins go down the value accrues to what they are owning (the business units/product areas).
- tim333 10mo agoThey were working on adapting GPUs for machine learning back in 2005. The getting lucky with AI was preceded by a lot of preparation.