The NVIDIA RTX 3080 Ti, while a powerful GPU, falls short of the VRAM requirements for the FLUX.1 Schnell model. FLUX.1 Schnell, with its 12 billion parameters, necessitates 24GB of VRAM when running in FP16 (half-precision floating point). The RTX 3080 Ti only offers 12GB of GDDR6X memory. This 12GB deficit means the model cannot be loaded and executed directly on the GPU without employing significant workarounds. The RTX 3080 Ti's memory bandwidth of 0.91 TB/s is substantial, but irrelevant when the entire model cannot reside in GPU memory. CUDA cores and Tensor cores are also rendered ineffective due to the VRAM limitation.
Due to the VRAM limitation, running FLUX.1 Schnell directly on the RTX 3080 Ti is not feasible without significant modifications. Consider these options: 1) **Quantization:** Explore aggressive quantization techniques like 4-bit or even 2-bit quantization (using libraries like `bitsandbytes` or `GPTQ`) to reduce the model's memory footprint. This will drastically reduce VRAM usage, but can impact output quality. 2) **Offloading:** Utilize CPU offloading, where portions of the model are processed on the CPU. This will be significantly slower but allows the model to run. Libraries like `accelerate` in Hugging Face provide tools for this. 3) **Model Splitting:** If possible, explore splitting the model across multiple GPUs. 4) **Alternative Models:** Consider smaller diffusion models that fit within the RTX 3080 Ti's VRAM. Be aware that each of these options will impact performance and potentially output quality.