The primary limiting factor for running Llama 3.3 70B on the AMD RX 7900 XTX is the available VRAM. Llama 3.3 70B in FP16 precision requires approximately 140GB of VRAM to load the model weights and manage activations during inference. The RX 7900 XTX only provides 24GB of VRAM, resulting in a significant shortfall of 116GB. This discrepancy means the entire model cannot be loaded onto the GPU simultaneously, preventing direct inference. The memory bandwidth of 0.96 TB/s on the RX 7900 XTX is substantial, but irrelevant when the model cannot fit in memory. The absence of dedicated Tensor Cores on the AMD GPU also impacts performance, as optimized tensor operations are crucial for LLM inference acceleration.
Given the VRAM limitations, directly running Llama 3.3 70B on the RX 7900 XTX is not feasible without significant compromises. Consider quantization techniques such as 4-bit or 8-bit to reduce the model's memory footprint. Even with quantization, offloading layers to system RAM might be necessary, which will drastically reduce inference speed. Another option is to use a distributed inference setup, splitting the model across multiple GPUs or machines. If high performance is crucial, consider using a GPU with significantly more VRAM, such as an NVIDIA A100 or H100, or cloud-based inference services.