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No - because this eliminates entirely or shifts the majority of work from GPU to CPU - and Nvidia does not sell CPUs. I'm not even sure how to reply to this. GPUs are fundamentally much more efficient for AI inference than CPUs.
I think SIMD is not so much better than SIMT for solved problems as a level in claiming a problem as solved.
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The cost for interactive video would be a requirement of 10x bandwidth, basically to cover idle time. Not efficient but not impossible, and definitely wouldn’t change ISP business models. The latency benefit would outweigh the cost. Just absolutely instant video interaction.
Einstein would like to have a word… And for the little bit of impact queueing latency has ( if done well , i.e. no bufferbloat), I doubt anyone would notice the difference, honestly.
The description of DeepSeek reminds me of my experience in networking in the late 80s - early 90s. Back then a really big motivator for Asynchronous Transfer Mode (ATM) and fiber-to-the-home was the promise of video on demand, which was a huge market in comparison to the Internet of the day. Just about all the work in this area ignored the potential of advanced video coding algorithms, and assumed that broadcast TV-q…
That only lasted so long. Then it was heavy machinery (hydraulics, excavators, etc)
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Interest rates have an effect too, Uber and Airbnb were starting in a much more fundraising friendly time.
High interest rates are supposed to force the remaining businesses out there to be profitable, so in theory, the startups of today should be far faster to profitability or they burn out.
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Don't forget that "CUDA" involves more than language constructs and programming paradigms. With NVDA, you get tools to deploy at scale, maximize utilization, debug errors and perf issues, share HW between workflows, etc. These things are not cheap to develop.
It might not be cheap to develop them but if you can save $10B in hardware costs by doing so you're probably looking at positive ROI.
Oh wait, it takes years to do all that and in the meantime you're wasting energy on not staying at the forefront of a hot tech trend.
Looks like huge astroturfing effort from CCP. I am seeing these coordinated propaganda inside every AI related sub on reddit, on social media and now - here.
Yeah I get that feeling too. Lots of old school astroturfing going on.
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> NVIDIAs moat Offtopic, but your comment finally pushed me over the edge to semantic satiation [1] regarding the word "moat". It is incredible how this word turned up a short while ago and now it seems to be a key ingredient of every second comment. [1] https://en.wikipedia.org/wiki/Semantic_satiation
The word moat was first used in english in the 15th century https://www.merriam-webster.com/dictionary/moat
I did not mean that it was literally invented a short while ago - a few months ago I had to look up what it means though (not native English).
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I can see close to zero possibility that the majority of the work will be shifted to the CPU. Anything a CPU can do can just be done better with specialised GPU hardware.
> Anything a CPU can do can just be done better Nope. Anything inheriantly serial is better off on the CPU due to caching and it's architecture. Many things that are highly parallizable are getting GPU enabled. Games and ML are GPU by default, but many things are migrating to CUDA. You need both for cheap, high performance computing. They are different workloads.
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I think SIMD is not so much better than SIMT for solved problems as a level in claiming a problem as solved.
What do you think GPUs are? Basically SIMD asics.
Whether your new coprocessor or instructions look more like a GPU or something else doesn't really matter if we are done squinting and calling it graphics like problems and/or claiming it needs a lot more than a middle class PC.
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Can you say more about using RL at inference time, ideally with a pointer to read more about it? This doesn’t fit into my mental model, in a couple of ways. The main way is right in the name: “learning” isn’t something that happens at inference time; inference is generating results from already-trained models. Perhaps you’re conflating RL with multistage (e.g. “chain of thought”) inference? Or maybe you’re talking ab…
I wasn't clear. Model weights aren't changing at inference time. I meant at inference time the model will output a sequence of thoughts and actions to perform tasks given to it by the user. For instance, to answer a question it will search the web, navigate through some sites, scroll, summarize, etc. You can model this as a game played by emitting a sequence of actions in a browser. RL is the technique you want to tr…