[P] Gemma 4 running on NVIDIA B200 and AMD MI355X from the same inference stack, 15% throughput gain over vLLM on Blackwell
Google DeepMind dropped Gemma 4 today: Gemma 4 31B: dense, 256K context, redesigned architecture targeting efficiency and long-context quality Gemma 4 26B A4B: MoE, 26B total / 4B active per forward pass, 256K context Both are natively multimodal (text, image, video, dynamic resolution). We got both running on MAX on launch day across NVIDIA B200 and AMD MI355X from the same stack. On B200 we're seeing 15% higher output throughput vs. vLLM (happy to share more on methodology if useful). Free playground if you want to test without spinning anything up: https://www.modular.com/#playground submitted by /u/carolinedfrasca [link] [comments]
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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]

![[P] Gemma 4 running on NVIDIA B200 and AMD MI355X from the same inference stack, 15% throughput gain over vLLM on Blackwell](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-neural-network-P6fqXULWLNUwjuxqUZnB3T.webp)

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