The primary limiting factor for running large language models like Llama 3.1 405B is VRAM. This model, in FP16 precision, requires approximately 810GB of VRAM to load and operate efficiently. The AMD RX 7900 XTX, while a powerful gaming GPU, only offers 24GB of VRAM. This creates a significant shortfall of 786GB, making it impossible to load the model in its native FP16 format. The memory bandwidth of 0.96 TB/s on the 7900 XTX is substantial, but irrelevant when the model cannot fit into the available memory. Furthermore, the absence of dedicated Tensor Cores on the AMD GPU means that inference will rely on the GPU's compute units, leading to slower performance compared to GPUs with specialized AI acceleration hardware.
Even with techniques like offloading layers to system RAM, the sheer size of the model compared to the available VRAM will result in extremely slow inference speeds, making it impractical for real-time applications. The lack of CUDA cores, while not a direct impediment, means that CUDA-optimized inference frameworks will not be usable, restricting the choice of software. Without sufficient VRAM, estimating tokens per second or optimal batch size becomes meaningless as the model cannot even be initialized correctly.
Given the massive VRAM discrepancy, running Llama 3.1 405B directly on the RX 7900 XTX is not feasible. Instead, consider using a significantly smaller model that fits within the 24GB VRAM limit, or explore cloud-based solutions like Google Colab, AWS SageMaker, or similar services that offer access to GPUs with sufficient VRAM. Alternatively, if you have access to multiple GPUs, consider model parallelism to distribute the model across several GPUs, although this adds complexity to the setup and requires appropriate software and expertise.
If you are determined to use the RX 7900 XTX, extreme quantization techniques, such as 2-bit or 3-bit quantization combined with CPU offloading might allow you to load a heavily compressed version of the model. However, expect significant performance degradation and a reduction in model accuracy. Focus on optimizing for the smallest possible footprint, even if it means sacrificing quality.