The primary limiting factor in running large language models (LLMs) like Llama 3.1 70B is the amount of available VRAM on the GPU. Llama 3.1 70B, in its full FP16 precision, requires approximately 140GB of VRAM to load the model weights and perform inference. The AMD RX 7900 XTX, with its 24GB of GDDR6 VRAM, falls significantly short of this requirement. This means the model cannot be loaded in its entirety onto the GPU, leading to a compatibility failure. While the RX 7900 XTX boasts a memory bandwidth of 0.96 TB/s, which is generally favorable for data transfer, this bandwidth cannot compensate for the lack of sufficient on-device memory to hold the model. The absence of Tensor Cores on the AMD RX 7900 XTX also means that specialized acceleration for matrix multiplication operations, crucial for LLM inference, is not available, further impacting potential performance.
Given the VRAM limitations, directly running Llama 3.1 70B on the RX 7900 XTX in FP16 precision is not feasible. To make this model runnable, you'll need to drastically reduce its memory footprint through quantization. Consider using techniques like 4-bit or even 3-bit quantization with libraries like llama.cpp or ExLlamaV2. This will significantly reduce the VRAM requirement, potentially bringing it within the 24GB limit. However, be aware that extreme quantization levels can impact the model's accuracy and coherence. Another approach could be offloading some layers to system RAM, but this will dramatically reduce inference speed due to the slower transfer rates between system RAM and the GPU.