261–270 of 340 posts
It is already proven that good language models are possible given enough resources. The challenge now is to put these models in a solution which do not require unfathomable amounts of resources for the average use cases.
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I don't really understand the bird's-eye view of the product line, but judging by some of the raw physical numbers and configurations Jensen was bragging about, it means that they want to basically play the mainframe game of locking high-end applications into proprietary middleware running on proprietary chassis with proprietary cluster interconnect (hello, Mellanox acquisiton).
The lock-in is more of a bonus for them. The underlying problem is that it's impossible to build a chip big enough, or even a collection of chiplets big enough. Training LLMs requires more silicon than can fit on one PCB, so they need an interconnect that is as fast as possible. With interconnect bandwidth as a critical bottleneck, they're not going to wait around for the industry to standardize on a suitable interco…
I wonder when we as an industry will start to address the scaling issues in LLMs.It is obviously in Nvidias interest to keep pushing out bigger and better GPUs, but what is the collective interest? It is already proven that good language models are possible given enough resources. The challenge now is to put these models in a solution which do not require unfathomable amounts of resources for the average use cases.
This is not a problem with AI only, but with every software we use. Only two groups try to optimize things and try to fit into smaller systems. Passionate programmers and people who is paid to do this (e.g.: phone manufacturers' software teams, etc.).
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I get it, the service model always shines the brightest in the eye of the revenue calculator. But I have immediate skepticism they’ll be able to execute at a competitive level. Their core competency has always been manufacture and production, not service-based things. It’s a big rock to push up a tall hill.
Genuine question (I don't know much about cloud stuff): how is providing a cloud service/platform (at scale) even remotely as hard as designing, manufacturing and selling GPU's (including drivers and firmware) at massive scale? It feels like reading that setting up something like Facebook would be extremely challenging for a company like SpaceX.
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I get it, the service model always shines the brightest in the eye of the revenue calculator. But I have immediate skepticism they’ll be able to execute at a competitive level. Their core competency has always been manufacture and production, not service-based things. It’s a big rock to push up a tall hill.
Genuine question (I don't know much about cloud stuff): how is providing a cloud service/platform (at scale) even remotely as hard as designing, manufacturing and selling GPU's (including drivers and firmware) at massive scale? It feels like reading that setting up something like Facebook would be extremely challenging for a company like SpaceX.
Just because an organisation is extremely good at one thing doesn't mean it can easily apply that to another field. I would guess that SpaceX probably does have the talent on hand to throw together a Facebook clone, but equally I think they would struggle to actually complete with Facebook as a business at scale.
I haven't listened to Jensen speak before, but am I the only one who thought the presentation wasn't very polished? Not a knock on anything he has accomplished, just an observation that sorta surprised me
He said he didn't rehearse well. I think it makes him come across very genuinely, not some dumb hyperpolished corporate blabla
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I get it, the service model always shines the brightest in the eye of the revenue calculator. But I have immediate skepticism they’ll be able to execute at a competitive level. Their core competency has always been manufacture and production, not service-based things. It’s a big rock to push up a tall hill.
Genuine question (I don't know much about cloud stuff): how is providing a cloud service/platform (at scale) even remotely as hard as designing, manufacturing and selling GPU's (including drivers and firmware) at massive scale? It feels like reading that setting up something like Facebook would be extremely challenging for a company like SpaceX.
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CUDA is not open. See what happened with ZLUDA.
I'm not sure your implication. My understanding of the project is AMD didn't want to invest in it anymore.
My take from being at the keynote and the content I've seen so far at the conference is that Nvidia's is moving up the stack (like all good hardware vendors are prone to do). Obviously they are going to keep doing bigger. But the takeaway for me is that they are building "docker for llms" - NIM. They are building a container system where you can download/buy(?) NIMs and easily deploy them on their hardware. Going to…
Won't do anything to most consumer facing AI, the UI & convenience is already a major selling point. A bigger threat is that the feature the business is built around makes it into mainline software... there is no demand for (paid) background removal anymore as every iPhone can do it nowadays. Generally if whatever AI product you have can easily just be a feature in whatever application businesses already use, then yo…
Proper background removal of even remotely complex content is still in demand, especially on a large scale. I doubt you'd use an iPhone to work on >100 of images per second.