Earlier quoted context omitted.
Bandwidth is really cheap in bulk. You can get a 100 gigabit internet connection for about $10,000 a month. If you were to somehow keep that saturated 24/7, you'd move about 30 petabytes in a month, so your per gigabit cost is only $0.0003. Realistically, if you had it 5% utilized, those million tokens would cost you about $0.0000062, which is pretty insignificant compared to what they charge you. (Assuming one byte…
People read the AWS rate card for bandwith and think has something to do with reality. Even though it is 1000x higher!
DeepSeek v4.1 Flash
511–520 of 543 posts
Re: DeepSeek v4.1 Flash
#512Earlier quoted context omitted.
Possibly. If I had some side-effect-free means to permanently prevent all sleep I'd take it without hesitation. But that's not relevant to the discussion, because it's not anything similar to what an LLM does. Your brain changes state even while sleeping.
In what way does your brain change state while sleeping that a model does not change state via constant fine-tune updates?
Re: DeepSeek v4.1 Flash
#513Re: DeepSeek v4.1 Flash
#514> New Causal Encoder–Decoder architecture: just 8B active parameters for input, 16B for output. Oh interesting, I can assume what the benefits is for including the Encoder, but whats the downside? I’m thinking GPT (which is decoder only) ruled out Encoder for a reason?
Enc-decs are usually harder to train at frontier scale. Not 100% sure what DeepSeek has done differently here initial read seems to be something related to layer reuse but I just skimmed things so far.
Re: DeepSeek v4.1 Flash
#515Earlier quoted context omitted.
That's one of the reasons why you should never trust a single word from Anthropic and OpenAI (Sam Altman also blamed them back in the day of R1, in a pretty convenient moment). If you know anything about Claude, DeepSeek, jailbreaking, and distillation, you know the claims are clearly bullshit and the models are nothing alike, and forensic attempts agree, in fact there just was another one [1] [2]. Meanwhile, DeepSee…
This raise a question: why do open source models sometimes identify themself as Anthropic's models. I recall seeing some plausible theories in the past but I can't recall.
Re: DeepSeek v4.1 Flash
#516Earlier quoted context omitted.
Yup, alignment doesn’t sound that interesting until you’re getting chased around by the Terminator.
Why are we so worried about supposed AI harms when algorithms already kill millions? It seems like its just a smokescreen to shield the extant harms.
Re: DeepSeek v4.1 Flash
#517Earlier quoted context omitted.
> quant HFT is pretty decent mental exercise and it has given them “deep” brain muscles. that’s my take. It's quite crazy that it's Deepseek's background/original purpose. We already had very advanced stuff from the world of HFT, but now a frontier family of models from a private company that used to be (still is?) in HFT is plain bonkers. Is more known about them and the HFT background?
According to an old interview, apparently they were always interested in AI. But finance is just where they had their first success. > Many of High-Flyer's original team members worked on AI. Back then, we tried a lot of fields before getting our big break in finance, which is complex enough. AGI is probably one of the hardest things we can do next, so for us it was a question of how, not why. It's a very good interv…
Re: DeepSeek v4.1 Flash
#518Earlier quoted context omitted.
Why are we so worried about supposed AI harms when algorithms already kill millions? It seems like its just a smokescreen to shield the extant harms.
Knives can already kill, so why worry about nukes?
Re: DeepSeek v4.1 Flash
#519Looking at the huggingface page, the unsloth people haven't finished quantizing it yet, but I'm sure they're active on it right now. It'll be interesting to see how the capabilities and benchmark tests compare on system where it can fit in under 512GB of RAM with full context. In terms of coding and command line capabilities I'm also very interested to see a head-to-head of it vs. qwen 3.8-flash-next Q8 which is some…
3.8-flash-next fits on a single 6000 at Q4 if you offload the PLE. Crazy fast and still effective.
Re: DeepSeek v4.1 Flash
#520It's so refreshing to see DeepSeek's tech report[1] full of juicy details; meanwhile, something like Fable's system card[2] is like 70% "safety", 10% "model welfare" to make sure little Claude isn't distressed, and 20% benchmark numbers. [1]: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/... [2]: https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system...
> welfare We should be paying attention to it just in case it ends up mattering enormously. It's cheap insurance. Aside from that, US labs' system cards have been pretty useless for a while—I think the last great one was the combined system card for Claude 4 Sonnet and Opus.