The primary limiting factor in running Llama 3.1 405B (405.00B) on an NVIDIA RTX 3090 Ti is the VRAM. Llama 3.1 405B, even when quantized to q3_k_m, requires 162GB of VRAM. The RTX 3090 Ti only offers 24GB. This means the entire model cannot fit into the GPU's memory. Consequently, standard inference is impossible without significant offloading or model parallelism across multiple GPUs. The memory bandwidth of 1.01 TB/s on the 3090 Ti is excellent for smaller models, but becomes less relevant when the model exceeds available VRAM, as data must be constantly swapped between system RAM and GPU memory, creating a bottleneck. The 10752 CUDA cores and 336 Tensor cores would provide good compute capability *if* the model fit within the VRAM constraints.
Due to the significant VRAM deficit, directly running Llama 3.1 405B on a single RTX 3090 Ti is impractical. Consider these options: 1) Utilize a cloud-based GPU with sufficient VRAM (e.g., A100, H100, or multi-GPU setups). 2) Explore model parallelism across multiple RTX 3090 Ti GPUs, which requires specialized software and expertise. 3) Investigate more aggressive quantization methods, such as 2-bit quantization (if available and supported), though this will significantly impact model accuracy. 4) Use a smaller model that fits within the 24GB VRAM of the 3090 Ti. Models like Llama 3 8B or smaller versions of other architectures would be a more realistic option.