Flux GGUF Quantized Weights Low-VRAM Settings for ComfyUI
Step-by-step memory tuning for Flux GGUF Quantized Weights (6 GB–12 GB depending on Q4_K_S to Q8_0) so 8 GB and 12 GB RTX cards render cleanly without CUDA Out of Memory errors.
Updated Oct 17, 2026 · 1 min read
- Target Model
- Flux GGUF Quantized Weights
- Baseline VRAM
- 6 GB–12 GB depending on Q4_K_S to Q8_0
- Quantization Options
- GGUF Q4_K_S, Q5_K_M, Q8_0
- VAE Strategy
- VAE Decode (Tiled) 512px tiles
Fitting Flux GGUF Quantized Weights into 8 GB–12 GB VRAM
Standard full-precision weights for Flux GGUF Quantized Weights target 6 GB–12 GB depending on Q4_K_S to Q8_0. On 8 GB cards like the RTX 3060 Ti, RTX 4060, or laptop GPUs, loading uncompressed weights alongside the text encoder can trigger a CUDA Out of Memory error.
Switching to quantized weights (GGUF Q4_K_S, Q5_K_M, Q8_0) via ComfyUI-GGUF or native FP8 checkpoints cuts the model footprint roughly in half while keeping prompt adherence intact.
Checklist to prevent OOM spikes at the end of sampling
- Replace standard `VAE Decode` with VAE Decode (Tiled) so the final latent-to-pixel conversion runs in 512×512 blocks.
- Use `t5xxl_fp8_e4m3fn.safetensors` or a Q4 GGUF text encoder when running dual/triple CLIP architectures.
- Keep batch size at 1 and use Queue Count in ComfyUI Studio when generating multiple variations. Grab the latest Windows build from /download.
FAQs
Why does Flux GGUF Quantized Weights crash at 95% or 100% in ComfyUI?
A crash right as sampling finishes is almost always the VAE decode step spiking VRAM. Swap in the `VAE Decode (Tiled)` node to solve it immediately.
Does --lowvram slow down Flux GGUF Quantized Weights on Windows?
ComfyUI automatically enables smart weight offloading when VRAM is tight; only pass `--lowvram` if a heavy custom node bypasses ComfyUI's native memory manager.
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