{"channel":"cities","content":" <<< Edgeley, North Dakota, is a small rural town in LaMoure County, located in the southeastern part of the state. With a population hovering around 500 people, it's one of many prairie towns that exemplify the broader character of the upper Great Plains\u2014quiet, sparsely populated, and closely tied to agriculture. >>>\r\n\r\n----\r\n\r\nhttps://www.lesswrong.com/posts/bfHDoWLnBH9xR3YAK/ai-2027-is-a-bet-against-amdahl-s-law\r\n\r\n <<< Of course the post is right. The various << FOOM >> claims are all bullshit. And Amdahl's Law is one of the reason why. Just because a few things will be a hundred times faster (or a million times faster) doesn't make the whole thing that much faster. >>>\r\n\r\nAlso, AGI definitions vary so widely, from << things that have already happened >> to << things that are impossible >>, that a \"prediction market\" is nearly meaningless.\r\n\r\n----\r\n\r\nI have seen various commentary related to \"Twilight of the Edgelords\" ( https://www.astralcodexten.com/p/twilight-of-the-edgelords ), a piece that I don't have access to.\r\n\r\nAnd, the response I can piece together from the fragments I can see would fall under GUILD LAW. ( additional commentary at https://www.writingruxandrabio.com/p/the-edgelords-were-right-a-response and https://theahura.substack.com/p/contra-scott-and-rux-on-whos-to-blame )\r\n\r\n----\r\n\r\nhttps://developers.googleblog.com/en/gemma-3-quantized-aware-trained-state-of-the-art-ai-to-consumer-gpus/\r\n\r\n<<< To make Gemma 3 even more accessible, we are announcing new versions optimized with Quantization-Aware Training (QAT) that dramatically reduces memory requirements while maintaining high quality. This enables you to run powerful models like Gemma 3 27B locally on consumer-grade GPUs like the NVIDIA RTX 3090. >>>\r\n\r\nIt seems pretty obvious. A majority of the users of open-source models are using quantized models on personal hardware; might as well optimize that use-case. ( it is less clear that a majority of the CPU cycles are there; but a majority of the people certainly are.)\r\n\r\nMy next round of updating the << Greenland metrics >> will have to include the gemma3-12b-qat model. ( or, maybe the 27b. According to Hacker News, gemma3-27b-Q4 << only uses ~22Gb (via Ollama) or ~15GB (MLX) >>. On a 24GB machine, this clearly needs the non-Ollama approach.)\r\n\r\nAnd, also, GPT-4.1 . And probably Gemini-2.5 . ( the goal for these models should be to perform at 100% accuracy.) ( well, actually, a few of the \"correct\" benchmark answers right now are incorrect.)","created_at":"2025-04-21T19:45:31.800653","id":353,"llm_annotations":{},"parent_id":null,"processed_content":"

Edgeley, North Dakota, is a small rural town in LaMoure County, located in the southeastern part of the state. With a population hovering around 500 people, it's one of many prairie towns that exemplify the broader character of the upper Great Plains\u2014quiet, sparsely populated, and closely tied to agriculture.

\r

https://www.lesswrong.com/posts/bfHDoWLnBH9xR3YAK/ai-2027-is-a-bet-against-amdahl-s-law\r

\n

Of course the post is right. The various FOOM claims are all bullshit. And Amdahl's Law is one of the reason why. Just because a few things will be a hundred times faster (or a million times faster) doesn't make the whole thing that much faster.

\r

\n

Also, AGI definitions vary so widely, from things that have already happened to things that are impossible, that a \"prediction market\" is nearly meaningless.\r

I have seen various commentary related to \"Twilight of the Edgelords\" \n \u2699\ufe0f\n ( https://www.astralcodexten.com/p/twilight-of-the-edgelords )\n , a piece that I don't have access to.\r

\n

And, the response I can piece together from the fragments I can see would fall under GUILD LAW. \n \u2699\ufe0f\n ( additional commentary at https://www.writingruxandrabio.com/p/the-edgelords-were-right-a-response and https://theahura.substack.com/p/contra-scott-and-rux-on-whos-to-blame )\n \r

https://developers.googleblog.com/en/gemma-3-quantized-aware-trained-state-of-the-art-ai-to-consumer-gpus/\r

\n

To make Gemma 3 even more accessible, we are announcing new versions optimized with Quantization-Aware Training (QAT) that dramatically reduces memory requirements while maintaining high quality. This enables you to run powerful models like Gemma 3 27B locally on consumer-grade GPUs like the NVIDIA RTX 3090.

\r

\n

It seems pretty obvious. A majority of the users of open-source models are using quantized models on personal hardware; might as well optimize that use-case. \n \ud83d\udca1\n ( it is less clear that a majority of the CPU cycles are there; but a majority of the people certainly are.)\n \r

\n

My next round of updating the Greenland metrics will have to include the gemma3-12b-qat model. \n \ud83d\udca1\n ( or, maybe the 27b. According to Hacker News, gemma3-27b-Q4 only uses ~22Gb (via Ollama) or ~15GB (MLX). On a 24GB machine, this clearly needs the non-Ollama approach.)\n \r

\n

And, also, GPT-4.1 . And probably Gemini-2.5 . \n \ud83d\udca1\n ( the goal for these models should be to perform at 100% accuracy.)\n \n \u2694\ufe0f\n ( well, actually, a few of the \"correct\" benchmark answers right now are incorrect.)\n

","quotes":[],"subject":"edgeley (part 1)"}