Double PyTorch Inference Speed for Diffusion Models Using Torch-TensorRT | NVIDIA Technical Blog - NVIDIA Developer
<a href="https://news.google.com/rss/articles/CBMiqwFBVV95cUxQZmN5cmtxaXgzbzUzVk1pT1JtY1dTOFh5ZUNZTmx2eDAwc2JUY29OczJSVXJfaDFxZkl5eWR2eUh6MlZyZGZDVG44b3RQdk1FS0ZLdC00cXRoUGFFVzR2Wng1YXFOZ0NWUE02ZFpidW1KNTYtYmppWk1zaHFjdGNqalFQTkdRaTdERl9HNmhrRTU5UmFPTGdSbEYzYjhhdGdvNnpEUnlKUWlFLWc?oc=5" target="_blank">Double PyTorch Inference Speed for Diffusion Models Using Torch-TensorRT | NVIDIA Technical Blog</a> <font color="#6f6f6f">NVIDIA Developer</font>
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Qwen 3.5 397B vs Qwen 3.6-Plus
I see a lot of people worried about the possibility of QWEN 3.6 397b not being released. However, if I look at the small percentage of variation between 3.5 and 3.6 in many benchmarks, I think that simply quantizing 3.6 to "human" dimensions (Q2_K_XL is needed to run on an RTX 6000 96GB + 48GB) would reduce the entire advantage to a few point zeros. I'm curious to see how the smaller models will perform towards Gemma 4, where competition has started. submitted by /u/LegacyRemaster [link] [comments]



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