The AMD RX 7900 XTX, equipped with 24GB of GDDR6 VRAM, presents a marginal compatibility scenario for the FLUX.1 Dev model, which requires precisely 24GB of VRAM when running in FP16 (half-precision floating point). This tight VRAM constraint leaves no headroom for other processes or potential memory fragmentation, increasing the likelihood of out-of-memory errors, especially when dealing with larger batch sizes or complex inference pipelines. While the 7900 XTX boasts a substantial 0.96 TB/s memory bandwidth, the RDNA 3 architecture lacks dedicated Tensor Cores, which are optimized for accelerating matrix multiplications crucial for deep learning. This absence may lead to lower inference speeds compared to NVIDIA GPUs with similar VRAM capacity but equipped with Tensor Cores.
Given the limited VRAM headroom, running FLUX.1 Dev on the RX 7900 XTX will require careful optimization. Start by using a framework optimized for AMD GPUs, such as ROCm or a framework that leverages the HIP API. Quantization is highly recommended. Consider using 8-bit integer quantization (INT8) or even 4-bit quantization (bitsandbytes or similar) to significantly reduce the VRAM footprint and potentially improve performance. Experiment with different batch sizes, starting with a batch size of 1 and gradually increasing it while monitoring VRAM usage. Be aware that performance will likely be lower than comparable NVIDIA cards due to the lack of Tensor Cores. If you encounter persistent out-of-memory errors or unacceptable performance, consider offloading some layers to system RAM or exploring distributed inference across multiple GPUs if available.