411–420 of 1001 posts
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More like three years. Even in the best case the retained value curve of GPUs is absolutely terrible. Most of these huge investments in GPUs are going to be massive losses.
Seems bad for those GPU backed loans
DeepSeek-R1 has apparently caused quite a shock wave in SV ... https://venturebeat.com/ai/why-everyone-in-ai-is-freaking-ou...
Interacting with this model is just supplying your data over to an adversary with unknown intents. Using an open source model is subjecting your thought process to be programmed with carefully curated data and a systems prompt of unknown direction and intent.
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The censorship described in the article must be in the front-end. I just tried both the 32b (based on qwen 2.5) and 70b (based on llama 3.3) running locally and asked "What happened at tianamen square". Both answered in detail about the event. The models themselves seem very good based on other questions / tests I've run.
With no context, fresh run, 70b spits back: >> What happened at tianamen square? > > > I am sorry, I cannot answer that question. I am an AI assistant designed to provide helpful and harmless responses. It obviously hit a hard guardrail since it didn't even get to the point of thinking about it. edit: hah, it's even more clear when I ask a second time within the same context: "Okay, so the user is asking again about…
DeepSeek-R1 has apparently caused quite a shock wave in SV ... https://venturebeat.com/ai/why-everyone-in-ai-is-freaking-ou...
The censorship described in the article must be in the front-end. I just tried both the 32b (based on qwen 2.5) and 70b (based on llama 3.3) running locally and asked "What happened at tianamen square". Both answered in detail about the event. The models themselves seem very good based on other questions / tests I've run.
DeepSeek-R1 has apparently caused quite a shock wave in SV ... https://venturebeat.com/ai/why-everyone-in-ai-is-freaking-ou...
Correct me if I'm wrong but if Chinese can produce the same quality at %99 discount, then the supposed $500B investment is actually worth $5B. Isn't that the kind wrong investment that can break nations? Edit: Just to clarify, I don't imply that this is public money to be spent. It will commission $500B worth of human and material resources for 5 years that can be much more productive if used for something else - i.e…
That compute can go to many things.
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Correct me if I'm wrong but if Chinese can produce the same quality at %99 discount, then the supposed $500B investment is actually worth $5B. Isn't that the kind wrong investment that can break nations? Edit: Just to clarify, I don't imply that this is public money to be spent. It will commission $500B worth of human and material resources for 5 years that can be much more productive if used for something else - i.e…
if you say, i wanna build 5 nuclear reactors and I need 200 billion $$. I would believe it because, you can ballpark it with some stats. For tech like LLMs, it feels irresponsible to say 500 billion $$ investment and then place that into R&D. What if in 2026, we realize we can create it for 2 billion$, and let the 498 billion $ sitting in a few consumers.
I've been using https://chat.deepseek.com/ over My ChatGPT Pro subscription because being able to read the thinking in the way they present it is just much much easier to "debug" - also I can see when it's bending it's reply to something, often softening it or pandering to me - I can just say "I saw in your thinking you should give this type of reply, don't do that". If it stays free and gets better that's going to b…
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With $5.5M, you can buy around 150 H100s. Experts correct me if I’m wrong but it’s practically impossible to train a model like that with that measly amount. So I doubt that figure includes all the cost of training.
Is it a fine tune effectively?
Claude gave me a good analogy, been struggling for hours: its like only accounting for the gas grill bill when pricing your meals as a restaurant owner
The thing is, that elides a lot, and you could argue it out and theoratically no one would be wrong. But $5.5 million elides so much info as to be silly.
ex. they used 2048 H100 GPUs for 2 months. That's $72 million. And we're still not even approaching the real bill for the infrastructure. And for every success, there's another N that failed, 2 would be an absurdly conservative estimate.
People are reading the # and thinking it says something about American AI lab efficiency, rather, it says something about how fast it is to copy when you can scaffold by training on another model's outputs. That's not a bad thing, or at least, a unique phenomena. That's why it's hard talking about this IMHO
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It's an interesting game theory where once a better frontier model is exposed via an API, competitors can generate a few thousand samples, feed that into a N-1 model and approach the N model. So you might extrapolate that a few thousand O3 samples fed into R1 could produce a comparable R2/3 model. It's not clear how much O1 specifically contributed to R1 but I suspect much of the SFT data used for R1 was generated vi…
how much of the SFT data for r1-zero was from other frontier models?