421–430 of 500 posts
Exciting times to be living in .
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Doesn’t your point about video compression tech support Nvidia’s bull case? Better video compression led to an explosion in video consumption on the Internet, leading to much more revenue for companies like Comcast, Google, T-Mobile, Verizon, etc. More efficient LLMs lead to much more AI usage. Nvidia, TSMC, etc will benefit.
No - because this eliminates entirely or shifts the majority of work from GPU to CPU - and Nvidia does not sell CPUs. If the AI market gets 10x bigger, and GPU work gets 50% smaller (which is still 5x larger than today) - but Nvidia is priced on 40% growth for the next ten years (28x larger) - there is a price mismatch. It is theoretically possible for a massive reduction in GPU usage or shift from GPU to CPU to bene…
DeepSeek just further reinforces the idea that there is a first-move disadvantage in developing AI models. When someone can replicate your model for 5% of the cost in 2 years, I can only see 2 rational decisions: 1) Start focusing on cost efficiency today to reduce the advantage of the second mover (i.e. trade growth for profitability) 2) Figure out how to build a real competitive moat through one or more of the foll…
DeepSeek is profitable, openai is not. That big expensive moat won't help much when the competition knows how to fly.
Even if DeepSeek has figured out how to do more (or at least as much) with less, doesn't the Jevons Paradox come into play? GPU sales would actually increase because even smaller companies would get the idea that they can compete in a space that only 6 months ago we assumed would be the realm of the large mega tech companies (the Metas, Googles, OpenAIs) since the small players couldn't afford to compete. Now that st…
Selling 100 chips for $1 profit is less profitable than selling 20 chips for $10 profit.
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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
I'm struggling to understand how a moat can have a CRACK in it.
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Unique, ye, but isn't their method open? I read something about a group replicating a smaller variant of their main model.
Which brings the question, if LLMs are an asset of such strategic value, why did China allow the DeepSeek to be released? I see two possibilities here, either that the CCP is not that all-reaching as we think, or that the value of the technology isn't critical, and that the release was further cleared with the CCP and maybe even timed to come right after Trump's announcement of American AI supremacy.
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Because at some point, someone decided that 8 kbps makes for an acceptable audio stream per subscriber. And at first, the novelty of being able to call anyone anywhere, even with this awful quality, was novel enough that people would accept it. And most people did until the carriers decided they could allocate a little more with VoLTE, if it works on your phone in your area.
> Because at some point, someone decided that 8 kbps makes for an acceptable audio stream per subscriber. Has it not been like this for a very long time? I was under the impression that "voice frequency" being defined as up to 4 kHz was a very old standard - after all, (long-distance) phone calls have always been multiplexed through coaxial or microwave links. And it follows that 8kbps is all you need to losslessly d…
The speech codecs are complex and fascinating, very different from just doing a frequency filter and compressing.
The base is linear predictive coding, which encodes the voice based on a simple model of the human mouth and throat. Huge compression but it sounds terrible. Then you take the error between the original signal and the LPC encoded signal, this waveform is compressed heavily but more conventionally and transmitted along with the LPC signal.
Phones also layer on voice activity detection, when you aren't talking the system just transmits noise parameters and the other end hears some tailored white noise. As phone calls typically have one person speaking at a time and there are frequent pauses in speech this is a huge win. But it also makes mistakes, especially in noisy environments (like call centers, voice calls are the business, why are they so bad?). When this happens the system becomes unintelligible because it isn't even trying to encode the voice.
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Because at some point, someone decided that 8 kbps makes for an acceptable audio stream per subscriber. And at first, the novelty of being able to call anyone anywhere, even with this awful quality, was novel enough that people would accept it. And most people did until the carriers decided they could allocate a little more with VoLTE, if it works on your phone in your area.
> Because at some point, someone decided that 8 kbps makes for an acceptable audio stream per subscriber. Has it not been like this for a very long time? I was under the impression that "voice frequency" being defined as up to 4 kHz was a very old standard - after all, (long-distance) phone calls have always been multiplexed through coaxial or microwave links. And it follows that 8kbps is all you need to losslessly d…
The 8kbps rates on cellular are the more complicated (relative to G.711) AMR-NB encoding. AMR supports voice rates from about 5-12kbps with a typical 8kbps rate. There's a lot more pre and post processing of the input signal and more involved encoding. There's a bit more voice information dropped by the encoder.
Part of the quality problem even today with VoLTE is different carriers support different profiles and calls between carriers will often drop down to the lowest common codec which is usually AMR-NB. There's higher bitrate and better codecs available in the standard but they're implemented differently by different carriers for shitty cellular carrier reasons.
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My interpretation is that yes in the long haul, lower energy/hardware requirements might increase demand rather than decrease it. But right now, DeepSeek has demonstrated that the current bottleneck to progress is _not_ compute, which decreases the near term pressure on buying GPUs at any cost, which decreases NVIDIA's stock price.
Short term, I 100% agree, but remains to be seen what "short" means. According to at least some benchmarks, Deepseek is two full orders of magnitude cheaper for comparable performance. Massive. But that opens the door for much more elaborate "architectures" (chain of thought, architect/editor, multiple choice) etc, since it's possible to run it over and over to get better results, so raw speed & latency will still ma…
It's also worth keeping in mind that depending on benchmark, these values change (and can shrink quite a bit)
And it's also worth keeping in mind that the drastic drop in training cost(if reproducible) will mean that training is suddenly affordable for a much larger number of organizations.
I'm not sure the impact on GPU demand will be as big as people assume.