The primary limiting factor for running large language models (LLMs) like Llama 3.1 405B is VRAM capacity. The AMD RX 7900 XTX, while a powerful gaming GPU, has 24GB of VRAM. Even with aggressive quantization (Q4_K_M), the Llama 3.1 405B model requires approximately 202.5GB of VRAM to load the entire model into the GPU memory. This creates a significant shortfall of 178.5GB, making direct inference impossible. While the RX 7900 XTX boasts a respectable memory bandwidth of 0.96 TB/s, this is irrelevant when the model cannot fit within the available VRAM. The lack of dedicated Tensor Cores on the AMD architecture, compared to NVIDIA GPUs, further reduces potential inference speed, even if VRAM capacity wasn't a bottleneck.
Given the VRAM limitations, the model would need to be offloaded to system RAM (CPU), which would severely impact performance. CPU inference is significantly slower due to lower bandwidth and higher latency compared to GPU memory. The expected tokens per second would be minimal, making real-time or interactive applications impractical. Furthermore, the context length of 128000 tokens, while impressive, becomes unusable in this scenario due to the inability to load the model efficiently.
Unfortunately, running Llama 3.1 405B on a single AMD RX 7900 XTX is not feasible due to insufficient VRAM. The only practical option is to use a smaller model that fits within the 24GB VRAM capacity of the GPU. Consider using Llama 3 8B or 70B models, or other models with similar parameter sizes, and quantize them appropriately to fit within the available VRAM. Alternatively, explore cloud-based inference services that offer access to GPUs with larger VRAM capacities, such as NVIDIA A100 or H100. Distributed inference across multiple GPUs, though technically possible, requires significant engineering effort and is likely not practical for most users.