| Model: Abiray-Qwen3.6-27B-NVFP4.gguf - Legion 7i Gen10 - NVIDIA GeForce RTX™ 5090 - Intel® Core™ Ultra 9 275HX × 24 - RAM 32.0 GiB llamacpp settings: My successfull build details:
llamacpp version: b8999 Prompts I used from previous post Qwen3.6-27B-Q6_K can also be accessed at: https://www.reddit.com/r/LocalLLaMA/comments/1szp96f/qwen3627bq6_k_images/
I pasted the SVGs on black and white backgrounds and picked the most visually appealing. Conclusion: - 37 t/s - lower creativity of the model is visible in the images. - images are kinda looking kids cartoons, or simple compared to Q6_K(was also not some industry standards but i prefer q6) [link] [comments] |
Qwen3.6-27B-NVFP4 - images
Reddit r/LocalLLaMA / 5/2/2026
💬 OpinionDeveloper Stack & InfrastructureSignals & Early TrendsTools & Practical UsageModels & Research
Key Points
- The post shares a successful local inference setup for the Qwen3.6-27B-NVFP4 model using a specific Abiray-Qwen3.6-27B-NVFP4.gguf file.
- It lists the exact llama.cpp server launch parameters and the hardware/software environment (Legion 7i Gen10 with an RTX 5090, Core Ultra 9 275HX, 32GB RAM), including NVFP4-focused settings.
- The author provides detailed build steps for llama.cpp with CUDA enabled and NVFP4 turned on, including compilation flags (AVX-512/VNNI, CUDA F16, CUDA graphs) and toolchain versions.
- A build verification section confirms that NVFP4 tensor-core support (Blackwell FP4) and related backends (GPU and CPU shared libraries) were compiled and activated.
- Example prompts demonstrate generating SVG images via the configured server, indicating the model’s multimodal/image-generation style usage in this setup.
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