The AMD RX 7900 XTX, while a powerful gaming GPU, faces significant limitations when running large language models like Llama 3.1 70B. The primary bottleneck is VRAM capacity. Llama 3.1 70B in FP16 precision requires approximately 140GB of VRAM, and even when quantized to INT8, it still demands around 70GB. The RX 7900 XTX is equipped with only 24GB of GDDR6 VRAM, resulting in a substantial VRAM deficit of 46GB. This means the entire model cannot be loaded onto the GPU for processing, leading to a compatibility failure.
Even if aggressive quantization techniques were employed, the limited VRAM would severely restrict batch sizes and context lengths, resulting in extremely slow inference speeds. The memory bandwidth of 0.96 TB/s, while respectable, is secondary to the VRAM limitation in this scenario. Furthermore, the absence of dedicated tensor cores on the RX 7900 XTX means that the model would rely on the GPU's compute units, which are less optimized for the matrix multiplications that are fundamental to LLM inference. This lack of optimization would further impact performance, making real-time or even near-real-time inference impractical.
Given that AMD GPUs utilize ROCm instead of CUDA, specific optimizations need to be considered. ROCm support for Llama 3 models is available, but the VRAM limitation remains the primary issue. Without sufficient VRAM, the model cannot be effectively utilized, regardless of the underlying architecture or optimization efforts.
Due to the significant VRAM shortfall, running Llama 3.1 70B on a single AMD RX 7900 XTX is not feasible. Consider using cloud-based services like NelsaHost that offer instances with GPUs possessing sufficient VRAM, such as those equipped with NVIDIA A100 or H100 GPUs. Alternatively, explore distributed inference solutions that split the model across multiple GPUs, although this adds complexity and requires specialized software and hardware configurations.
Another option is to investigate smaller language models that fit within the 24GB VRAM limit of the RX 7900 XTX. Models with fewer parameters, such as Llama 3 8B or similar sized models, might be viable. If you are set on using the 70B model, consider extreme quantization methods like 4-bit quantization (Q4), but be aware that this will result in a significant loss of accuracy and the tokens/sec would likely still be very low.