SINDy-RL for interpretable and efficient model-based reinforcement learning - Nature
SINDy-RL for interpretable and efficient model-based reinforcement learning Nature
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AI Is Insatiable
While browsing our website a few weeks ago, I stumbled upon “ How and When the Memory Chip Shortage Will End ” by Senior Editor Samuel K. Moore. His analysis focuses on the current DRAM shortage caused by AI hyperscalers’ ravenous appetite for memory, a major constraint on the speed at which large language models run. Moore provides a clear explanation of the shortage, particularly for high bandwidth memory (HBM). As we and the rest of the tech media have documented, AI is a resource hog. AI electricity consumption could account for up to 12 percent of all U.S. power by 2028. Generative AI queries consumed 15 terawatt-hours in 2025 and are projected to consume 347 TWh by 2030. Water consumption for cooling AI data centers is predicted to double or even quadruple by 2028 compared to 2023. B
Anyone got Gemma 4 26B-A4B running on VLLM?
If yes, which quantized model are you using abe what’s your vllm serve command? I’ve been struggling getting that model up and running on my dgx spark gb10. I tried the intel int4 quant for the 31B and it seems to be working well but way too slow. Anyone have any luck with the 26B? submitted by /u/toughcentaur9018 [link] [comments]
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Anyone got Gemma 4 26B-A4B running on VLLM?
If yes, which quantized model are you using abe what’s your vllm serve command? I’ve been struggling getting that model up and running on my dgx spark gb10. I tried the intel int4 quant for the 31B and it seems to be working well but way too slow. Anyone have any luck with the 26B? submitted by /u/toughcentaur9018 [link] [comments]
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