The NVIDIA RTX 3070 Ti, with its 8GB of GDDR6X VRAM, falls significantly short of the 24GB required to load and run the FLUX.1 Schnell model in FP16 precision. This memory shortfall will prevent the model from even being loaded onto the GPU, resulting in an out-of-memory error. While the RTX 3070 Ti's 6144 CUDA cores and 0.61 TB/s memory bandwidth are substantial for many AI tasks, the sheer size of FLUX.1 Schnell's 12 billion parameters necessitates a much larger VRAM capacity. The Ampere architecture's Tensor Cores would accelerate compatible operations, but this is irrelevant if the model cannot fit in memory. Even if aggressive quantization techniques are employed, fitting a 12B parameter model into 8GB of VRAM is highly unlikely without severely impacting performance or model fidelity.
Furthermore, the relatively short context length of 77 tokens for FLUX.1 Schnell is less of a concern than the VRAM limitation. While a longer context window generally allows for more coherent and contextually relevant outputs, the primary bottleneck here is the inability to even load the model. Memory bandwidth, while important for overall performance, becomes secondary when the model exceeds the available VRAM. In practical terms, attempting to run FLUX.1 Schnell on an RTX 3070 Ti without significant modifications will be unsuccessful.
Given the severe VRAM limitation, directly running FLUX.1 Schnell on the RTX 3070 Ti is not feasible. Consider exploring alternative diffusion models with smaller parameter counts that can fit within the 8GB VRAM. If using FLUX.1 Schnell is essential, investigate offloading layers to system RAM, although this will drastically reduce inference speed. Another option is to use cloud-based GPU instances with sufficient VRAM (e.g., NVIDIA A100, H100) to run the model remotely.
If you are set on running this model locally, the only realistic path forward is extreme quantization. Experiment with 4-bit or even 3-bit quantization using libraries like `bitsandbytes` or `AutoGPTQ`. This will significantly reduce the VRAM footprint, but will also impact the quality of the generated outputs. Be prepared for a noticeable drop in fidelity and coherence.