The AMD RX 7900 XT, while a powerful GPU for gaming, falls short when running the FLUX.1 Schnell diffusion model due to insufficient VRAM. FLUX.1 Schnell, with its 12 billion parameters, requires 24GB of VRAM for FP16 (half-precision floating point) inference. The RX 7900 XT is equipped with 20GB of GDDR6 memory, leaving a deficit of 4GB. This VRAM shortfall prevents the model from loading entirely onto the GPU, leading to out-of-memory errors or forcing the system to rely on significantly slower system RAM, which drastically reduces performance.
Furthermore, while the RX 7900 XT boasts a memory bandwidth of 0.8 TB/s, the limited VRAM is the primary bottleneck in this scenario. Even with efficient memory access, the model cannot operate effectively without sufficient space to reside on the GPU. The absence of dedicated Tensor Cores on the RX 7900 XT, unlike NVIDIA GPUs, means that the model will rely on general-purpose compute units, further impacting inference speed. Without sufficient VRAM, estimating the tokens/second or achievable batch size becomes irrelevant as the model will likely fail to run or perform unacceptably slowly.
Due to the VRAM limitation, running FLUX.1 Schnell on the AMD RX 7900 XT in its native FP16 precision is not feasible. Several strategies can be employed to mitigate this issue, though performance will inevitably be affected. Consider using quantization techniques, such as 8-bit integer quantization (INT8), which can significantly reduce the VRAM footprint of the model. Alternatively, explore offloading some layers of the model to system RAM, although this will introduce a substantial performance penalty. Another option is to explore smaller diffusion models with fewer parameters that fit within the RX 7900 XT's 20GB VRAM capacity.
If you must run FLUX.1 Schnell on this hardware, investigate using inference frameworks optimized for AMD GPUs and memory management. Look for frameworks that support model parallelism or layer offloading to distribute the model's workload across the GPU and system RAM. Be prepared for significantly reduced inference speeds compared to GPUs with adequate VRAM. Experiment with different quantization levels and batch sizes to find a balance between VRAM usage and performance.