The NVIDIA RTX 4000 Ada, while a capable card based on the Ada Lovelace architecture, falls short of the VRAM requirements for the FLUX.1 Dev model. FLUX.1 Dev, with its 12 billion parameters, demands 24GB of VRAM when using FP16 (half-precision floating point) data types. The RTX 4000 Ada only provides 20GB. This 4GB VRAM deficit will prevent the model from loading entirely onto the GPU, leading to out-of-memory errors. While the RTX 4000 Ada boasts 6144 CUDA cores and 192 Tensor cores, crucial for accelerating AI workloads, these cores become ineffective if the model cannot reside fully in the GPU memory. The memory bandwidth of 0.36 TB/s is adequate but becomes a bottleneck if data needs to be constantly swapped between system RAM and GPU memory due to insufficient VRAM.
Given the VRAM limitation, running FLUX.1 Dev directly on the RTX 4000 Ada in FP16 is not feasible. Consider using quantization techniques like Q4 or Q8 to significantly reduce the model's memory footprint. This involves representing the model's weights with fewer bits, thereby decreasing VRAM usage. Alternatively, explore offloading layers to system RAM, although this will severely impact performance due to the slower transfer speeds between system RAM and the GPU. If performance is critical, consider upgrading to a GPU with at least 24GB of VRAM, such as an RTX 3090, RTX 4080, or an equivalent professional-grade card like an NVIDIA A4000 or A5000.