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This article compares the performance of Bonsai 2 27B, Gemma 4 12B, and Qwen 3.5 9B models on generating a Japanese voxel pagoda using an RTX 5090, concluding that Bonsai offers superior intelligence and detail for its memory footprint, benefiting the local AI community.
The user has renamed a GitHub repository to HyperQwen to focus on optimizing Qwen model inference speeds on local hardware and is seeking testers with 4090s and 5090s GPUs for both Windows and Linux.
The user tested DFlash2 on the Qwen3.8 27B model, reporting improved inference speeds for code generation but with increased memory usage compared to MTP.
Reports on testing the RTX Pro 4500 GPU with Oculink, focusing on INT4 Autoround and NVFP4 W4A4 quantized models using PrismaQuant.
A detailed benchmark of Qwen 3.6 27B using VLLM across BF16, FP8, and NVFP4 quantizations, showing NVFP4 fastest for token generation but FP8 best for prompt processing, with practical advice on choosing the right quantization for coding tasks.