201–210 of 240 posts
The craziest part is how far MoE has come thanks to Qwen. This beats all those 72B dense models we’ve had before and runs faster than 14B model depending on how you off load your VRAM and CPU. That’s insane.
Out of context, but i honestly hate how HN let itself get so far behind the times that this is the sort of inane commentary we get on AI.
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The model is 80b parameters, but only 3b are activated during inference. I'm running the old 2507 Qwen3 30B model on my 8gb Nvidia card and get very usable performance.
Yes, but you don’t know which 3B parameters you will need, so you have to keep all 80B in your VRAM, or wait until correct 3B are loaded from NVMe->RAM->VRAM. And of course it could be different 3B for each next token.
Hmm. 80B. These days I am on the lookout for new models in the 32B range, since that is what fits and runs comfortably on my MacBook Pro (M4, 64GB). I use ollama every day for spam filtering: gemma3:27b works great, but I use gpt-oss:20b on a daily basis because it's so much faster and comparable in performance.
The craziest part is how far MoE has come thanks to Qwen. This beats all those 72B dense models we’ve had before and runs faster than 14B model depending on how you off load your VRAM and CPU. That’s insane.
Qwen isn't directing the forward progress of llms. SOTA llms have been moe since gpt-4. The og 4. Out of context, but i honestly hate how HN let itself get so far behind the times that this is the sort of inane commentary we get on AI.
Full set of open weight model results: https://brokk.ai/power-ranking?version=openround&models=ds-r...
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Could someone kindly point to a convenient all-on-one ELI5 of all these words? :')
The best primer I've seen is Andrej Karpathy's first video in his "zero to hero" series. It's worth following along with your own practice. https://karpathy.ai/zero-to-hero.html
Added Qwen3 Next to the Brokk Power Ranking Open Round (coding benchmark). It's roughly GPT-OSS-20b strength. Full set of open weight model results: https://brokk.ai/power-ranking?version=openround&models=ds-r...
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Could someone kindly point to a convenient all-on-one ELI5 of all these words? :')
Background: LLMs take your input, upscale it into a very high dimensional space, and then downscale it back to 1D at the end. This 1D list is interpreted as a list of probabilities -- one for each word in your vocabulary. i.e f(x) = downscale(upscale(x)). Each of downscale() and upscale() are parameterized (billions of params). I see you have a gamedev background, so as an example: bezier curves are parameterized fun…
Thanks for the tailored response! ^^
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Could someone kindly point to a convenient all-on-one ELI5 of all these words? :')
The best primer I've seen is Andrej Karpathy's first video in his "zero to hero" series. It's worth following along with your own practice. https://karpathy.ai/zero-to-hero.html
https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQ...
and other helpers like Artem Kirsanov:
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Their proprietary models are very good too and go under the radar, they never seem to appear on any benchmarks. Qwen3-coder-plus is significantly better than their open source qwen3, Qwen3 max also rivals the SOTA models
Are these actually different models vs just different names from the open weights releases?