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RTX 5080 + RTX 3090 Setup: 80+ Tok/s on Qwen 3.6 27B Q8

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By GPU Name Persistence-M, Volatile Uncorr. ECC, GPU-Util Compute M
| imil.net
A year ago, I bought an RTX 5080 for both gaming and AI experiments. Little did I know back then that I would be giving into the joys of local LLM setups. Fast forward 2026, Qwen 3.5, Gemma, Qwen 3.6, I needed more than 16GB. So I got myself a refurbished RTX 3090 with 24GB. I could then run Qwen 3.6 Q4 quants, first at ~30 tok/s, then 50-60 with MTP. Not bad. But still felt limited while my 5080 was barely used. So I began digging what kind of setup could take profit of those 2 cards together.
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Posit AI Blog: Training ImageNet with R

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By GPU Name Persistence-M, Volatile Uncorr. ECC, GPU-Util Compute M
| BooBoone
((( ImageNet Deng et al. 2009) is an image database organized according to the WordNet Miller 1995) hierarchy which, historically, has been used in computer vision benchmarks and research. However, it was not until AlexNet ) demonstrated the efficiency of deep learning using convolutional neural networks on GPUs that the computer-vision discipline turned to deep learning to achieve state-of-the-art models that revolutionized their field.
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[典型坑]ubuntu22.04，5080笔记本显卡，驱动不能正确识别显卡-CSDN博客

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By GPU Name Persistence-M, Volatile Uncorr. ECC, GPU-Util Compute M
| CSDN
原创 于 2025-08-16 00:56:37 发布 · 224 阅读 · 7 · 0 · CC 4.0 BY-SA版权 版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。 文章标签： #ubuntu #linux #nvidia驱动 环境配置 ubuntu22.04，5080laptop，安装了比较稳定的570.144驱动，不能正确识别显卡 $ nvidia-smi No devices were found 问题解决 看看显卡是否在pci总线，如果有下面输出是在的。 $ lspci | grep -i nvidia 02:00.0 VGA compatible controller: NVIDIA Corporation Device 2c59 (rev a1) 02:00.1 Audio device: NVIDIA Corporation Device 22e9 (rev a1) 如果没有，可能得使用独显模式 查看系统日志 $ journalctl -b | grep -i...
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