[dead]
Qwen3-Next
81–90 of 240 posts
Re: Qwen3-Next
#82The same week Oracle is forecasting huge data center demand and the stock is rallying. If these 10x gains in efficiency hold true then this could lead to a lot less demand for Nvidia, Oracle, Coreweave etc
https://en.wikipedia.org/wiki/Jevons_paradox
Sure but where is the demand going to come from? LLMs are already in every google search, in Whatsapp/Messenger, throughout Google workspace, Notion, Slack, etc. ChatGPT already has a billion users.
Plus penetration is already very high in the areas where they are objectively useful: programming, customer care etc. I just don't see where the 100-1000x demand comes from to offset this. Would be happy to hear other views.
Re: Qwen3-Next
#83The same week Oracle is forecasting huge data center demand and the stock is rallying. If these 10x gains in efficiency hold true then this could lead to a lot less demand for Nvidia, Oracle, Coreweave etc
If someone had to bet on an AI crash which I imagine would led to unused datacentres and cheap GPUs how would they invest their winnings to exploit these resources?
Assuming your question isn't rhetorical, massive Oracle Crypto Farm.
Re: Qwen3-Next
#84Earlier quoted context omitted.
How are you prepping the PDF data before shoving it into Qwen?
I just compress the file size as low as possible without losing the quality, didn't even know there was more ways to prep it. I do sometimes chop up the PDF into smaller pdfs with their own individual chapters
On Linux you can use pdftotext also if you are only concerned with the text.
Re: Qwen3-Next
#85Alibaba keeps releasing gold content I just tried Qwen3-Next-80B-A3B on Qwen chat, and it's fast! The quality seem to match Qwen3-235B-A22B. Quite impressive how they achieved this. Can't wait for the benchmarks at Artificial analysis According to Qwen Chat, Qwen3-Next has the following limits: Maximum context length: 262,144 tokens Max summary generation length: 32,768 tokens This is 2x higher on context length and…
My take on long context for many frontier models is not about support but the accuracy drops drastically as you increase the context. Even if a model claims to support 10M context, reality is it doesn’t perform well when you saturate. Curious to hear others perspective on this
This is my experience with Gemini. Yes, I really can put an entire codebase and all the docs and pre-dev discussions and all the inter-engineer chat logs in there.
I still see the model becoming more intoxicated as turn count gets high.
Re: Qwen3-Next
#86Earlier quoted context omitted.
https://en.wikipedia.org/wiki/Jevons_paradox
Sure but where is the demand going to come from? LLMs are already in every google search, in Whatsapp/Messenger, throughout Google workspace, Notion, Slack, etc. ChatGPT already has a billion users. Plus penetration is already very high in the areas where they are objectively useful: programming, customer care etc. I just don't see where the 100-1000x demand comes from to offset this. Would be happy to hear other vie…
Long running agents?
Re: Qwen3-Next
#87Earlier quoted context omitted.
https://en.wikipedia.org/wiki/Jevons_paradox
Sure but where is the demand going to come from? LLMs are already in every google search, in Whatsapp/Messenger, throughout Google workspace, Notion, Slack, etc. ChatGPT already has a billion users. Plus penetration is already very high in the areas where they are objectively useful: programming, customer care etc. I just don't see where the 100-1000x demand comes from to offset this. Would be happy to hear other vie…
If you can make an LLM solve a problem but from 100 different angles at the same time, that's worth something.
Re: Qwen3-Next
#88Alibaba keeps releasing gold content I just tried Qwen3-Next-80B-A3B on Qwen chat, and it's fast! The quality seem to match Qwen3-235B-A22B. Quite impressive how they achieved this. Can't wait for the benchmarks at Artificial analysis According to Qwen Chat, Qwen3-Next has the following limits: Maximum context length: 262,144 tokens Max summary generation length: 32,768 tokens This is 2x higher on context length and…
How are you prepping the PDF data before shoving it into Qwen?
Not OP, but we use the docling library to extract text and put it in markdown before storing for use with an LLM.
Re: Qwen3-Next
#89Earlier quoted context omitted.
https://en.wikipedia.org/wiki/Jevons_paradox
Sure but where is the demand going to come from? LLMs are already in every google search, in Whatsapp/Messenger, throughout Google workspace, Notion, Slack, etc. ChatGPT already has a billion users. Plus penetration is already very high in the areas where they are objectively useful: programming, customer care etc. I just don't see where the 100-1000x demand comes from to offset this. Would be happy to hear other vie…
We've seen several orders of magnitude improvements in cpus over the years, yet you try to do anything now and interaction is often slower than that on zx spectrum. We can easily fill in order of magnitude improvement and that's only going to create more demand. We can/will have models thinking for us all the time, in parallel and bother us with findings/final solutions only. There is no limit here really.
Re: Qwen3-Next
#90The same week Oracle is forecasting huge data center demand and the stock is rallying. If these 10x gains in efficiency hold true then this could lead to a lot less demand for Nvidia, Oracle, Coreweave etc
If someone had to bet on an AI crash which I imagine would led to unused datacentres and cheap GPUs how would they invest their winnings to exploit these resources?
If the price of inference drops through the floor all the AI wrapper companies become instantly more valuable. Cursor is living on borrowed time because their agents suck and they're coasting on first mover advantage with weak products in general, but their position would get much better with cheap inference.