The primary limiting factor for running the FLUX.1 Dev model (12B parameters) on an NVIDIA RTX 3070 is the GPU's VRAM capacity. FLUX.1 Dev, when operating in FP16 (half-precision floating point), requires approximately 24GB of VRAM to load the model and manage the inference process. The RTX 3070 is equipped with only 8GB of GDDR6 VRAM, resulting in a significant shortfall of 16GB. This discrepancy means the model cannot be loaded entirely onto the GPU, leading to out-of-memory errors or forcing the system to rely heavily on system RAM, which is considerably slower.
While the RTX 3070's memory bandwidth of 0.45 TB/s and 5888 CUDA cores are respectable for many tasks, they are secondary concerns in this scenario. Even if the model could be loaded, the limited VRAM would severely bottleneck performance. The Ampere architecture's Tensor Cores would be underutilized due to the constant swapping of data between the GPU and system memory. Consequently, real-time or even near real-time inference speeds are unlikely to be achievable without significant compromises.
Due to the substantial VRAM deficit, running FLUX.1 Dev on an RTX 3070 in FP16 is not feasible without employing aggressive quantization techniques. Consider quantizing the model to INT8 or even INT4. This will significantly reduce the VRAM footprint, potentially bringing it within the RTX 3070's 8GB limit. However, be aware that extreme quantization can impact the model's accuracy and output quality. Experiment with different quantization levels to find a balance between performance and fidelity.
Alternatively, explore offloading some layers of the model to the CPU. While this will further reduce performance, it might allow you to run the model, albeit slowly. Using inference frameworks optimized for CPU offloading, like llama.cpp, can help mitigate the performance hit. If these options prove insufficient, consider using a GPU with more VRAM or utilizing cloud-based inference services.