The primary limiting factor in running large language models (LLMs) like Llama 3.3 70B is the amount of available VRAM on the GPU. Llama 3.3 70B, when using FP16 (half-precision floating point), requires approximately 140GB of VRAM to load the model weights. The AMD RX 7900 XT has 20GB of VRAM, which is significantly less than the required amount. This means the entire model cannot fit into the GPU's memory, leading to a 'FAIL' verdict in compatibility.
Even if techniques like offloading some layers to system RAM were employed, the performance would be severely degraded. System RAM is significantly slower than VRAM, resulting in extremely slow inference speeds. While the RX 7900 XT boasts a memory bandwidth of 0.8 TB/s, this bandwidth is only relevant when the data resides within the VRAM. The absence of dedicated Tensor Cores on the AMD RX 7900 XT also impacts performance, as these cores are specifically designed to accelerate matrix multiplications, a core operation in deep learning. Without them, calculations fall back to the GPU's general-purpose compute units, further reducing performance.
Due to the substantial VRAM deficit, running Llama 3.3 70B directly on the AMD RX 7900 XT is not feasible without significant compromises. Consider using a smaller model that fits within the 20GB VRAM, such as a 7B or 13B parameter model. Alternatively, explore cloud-based solutions or services that offer access to GPUs with sufficient VRAM. If you are determined to run Llama 3.3 70B locally, investigate techniques like model quantization (e.g., 4-bit or 8-bit) and CPU offloading, but be aware that this will drastically reduce inference speed and may not provide a satisfactory user experience.
Another option is to use a multi-GPU setup, although the RX 7900 XT does not support NVLink or similar high-bandwidth interconnects that are ideal for splitting model weights across multiple GPUs. Therefore, the performance gains from a multi-GPU setup with RX 7900 XT cards would likely be limited. Finally, if possible, consider upgrading to a GPU with significantly more VRAM, such as an NVIDIA RTX 6000 Ada Generation or similar.