# FLUX.1-schnell on 8GB VRAM (AMD, no CUDA): the GGUF format mismatch that wastes hours

> Source: <https://discuss.huggingface.co/t/flux-1-schnell-on-8gb-vram-amd-no-cuda-the-gguf-format-mismatch-that-wastes-hours/177010#post_1>
> Published: 2026-06-21 02:20:14+00:00

Sharing this because it cost me real time and I haven’t seen it documented clearly anywhere: if you’re trying to run FLUX.1-schnell through stable-diffusion.cpp (the C++ inference engine, not ComfyUI) on a low-VRAM AMD card via Vulkan, GGUF source matters and the error message doesn’t tell you why.

The setup: AMD RX 580 8GB (Polaris/GCN4, 2017 card — no ROCm support, no CUDA obviously), running stable-diffusion.cpp compiled with -DGGML_VULKAN=ON, no DirectML, no cloud.

The gotcha:

city96’s FLUX GGUF builds on HF (the ones most tutorials link, since they’re the most popular) — only work inside ComfyUI with the ComfyUI-GGUF custom node. They will NOT load in sd-server / the stable-diffusion.cpp CLI.

leejet’s FLUX GGUF builds — these are the ones built for stable-diffusion.cpp specifically and actually load.

Using a city96 file in sd-server just gives:

[ERROR] main.cpp:92 - new_sd_ctx_t failed

No further explanation, no hint that it’s a packaging/quantization-method mismatch rather than a VRAM or flag problem. I spent a while assuming it was a memory issue before realizing the file itself was the wrong build.

Once I switched to leejet’s FLUX.1-schnell-gguf repo, it loaded fine. For an 8GB card, the practical split that works:

Diffusion model on VRAM (~6.5GB for q4_k)

CLIP_L on VRAM (~235MB)

T5XXL and VAE offloaded to system RAM (–clip-on-cpu --vae-on-cpu)

–vae-tiling is NOT optional — without it, VAE decode OOMs even with everything else offloaded correctly.

Full command and timing breakdown (T5XXL conditioning ~11s, sampling ~14min at 4 steps/1024x1024, VAE decode ~40s) here if useful: [GitHub - aivisionslab-studios/rx580-local-ai-guide: Complete guide to running local AI on AMD RX 580 8GB via Vulkan — llama.cpp, Ollama, OpenWebUI, Stable Diffusion. No CUDA. No cloud. Free. · GitHub](https://github.com/aivisionslab-studios/rx580-local-ai-guide)

Posting mainly because “GGUF doesn’t load” is a generic enough error that it’s easy to misdiagnose as a VRAM or driver problem instead of a quantization-source mismatch. Curious if anyone’s hit the same wall with other GGUF-quantized diffusion models from different sources.