Fix My PC
Running into VRAM bottlenecks or slow generation times? Here are strategies to optimize your setup for modern AI workflows.
1. Use Lower Precision Models (Quantization)
If you lack the VRAM for full FP16 models, consider using quantized versions (GGUF or NF4). These can drastically reduce memory footprint with minimal impact on final quality. FLUX Dev inside ComfyUI works wonderfully as an 8-bit GGUF on standard consumer cards.
Browse Guides on GGUF and optimization →2. Optimize ComfyUI Arguments
Adding arguments like --lowvram or --medvram to your ComfyUI startup bat script can prevent Out of Memory (OOM) errors by more aggressively offloading weights to system RAM, though it will mildly slow down generation speeds.
3. Hardware Upgrades
Local AI is heavily dependent on VRAM bandwidth and capacity. If you're consistently hitting bounds on 8GB cards, upgrading to a 16GB tier (such as the RTX 4070 Ti Super or 5080) or an enthusiast 24GB card (RTX 3090/4090) is the best reliable long-term solution.
Plan your hardware upgrade with ComputeAtlas.ai ↗