The NVIDIA RTX 3080 with 10GB of VRAM falls short of the 24GB VRAM requirement for the FLUX.1 Dev model when using FP16 (half-precision floating point) precision. This incompatibility stems directly from the model's size; FLUX.1 Dev, with its 12 billion parameters, necessitates a large memory footprint to store the model weights, activations, and intermediate tensors during the diffusion process. Insufficient VRAM results in out-of-memory errors, preventing the model from running. The RTX 3080's memory bandwidth of 0.76 TB/s, while substantial, is secondary to the primary constraint of VRAM capacity in this scenario. Even with efficient memory access, the model simply cannot fit within the available memory space.
Because the model exceeds the available VRAM, performance is not a relevant factor. Attempting to run FLUX.1 Dev on the RTX 3080 10GB without significant modifications will result in failure. The model's context length of 77 tokens is also not a limiting factor in this instance; the VRAM constraint is the primary bottleneck. The Ampere architecture of the RTX 3080 does offer Tensor Cores that could accelerate computations if the model were able to load, but these cores cannot compensate for the lack of memory.
Given the VRAM limitation, running FLUX.1 Dev on an RTX 3080 10GB directly is not feasible without significant modifications. Consider using a GPU with at least 24GB of VRAM, such as an RTX 3090, RTX 4080, or a professional-grade card like an A4000 or A5000. Alternatively, explore quantization techniques to reduce the model's memory footprint. Quantization involves converting the model's weights to lower precision (e.g., INT8), which can significantly reduce VRAM usage, although this may come at the cost of some accuracy.
If upgrading hardware is not an option, investigate methods like model parallelism or offloading layers to system RAM. Model parallelism splits the model across multiple GPUs, each handling a portion of the computation. Offloading layers to system RAM can free up VRAM, but this introduces a significant performance bottleneck due to the slower transfer speeds between GPU and system memory. These methods require advanced configuration and may not be straightforward for all users.