The primary limiting factor for running Llama 3.3 70B on an NVIDIA RTX 4080 is the significant disparity in VRAM. Llama 3.3 70B in FP16 (half-precision floating point) requires approximately 140GB of VRAM to load the model weights and perform inference. The RTX 4080, equipped with 16GB of GDDR6X VRAM, falls drastically short of this requirement. This means the entire model cannot reside on the GPU simultaneously, leading to out-of-memory errors if a naive approach is used. While the RTX 4080's memory bandwidth of 0.72 TB/s is respectable, it becomes less relevant when the model cannot fit within the GPU's memory. The Ada Lovelace architecture and the presence of Tensor Cores would be beneficial for accelerating computations *if* the model could be loaded.
To run Llama 3.3 70B on an RTX 4080, you'll need to employ techniques that significantly reduce the VRAM footprint. Quantization is essential. Consider using 4-bit or even 3-bit quantization methods (e.g., QLoRA, bitsandbytes integration with Hugging Face Transformers) to drastically compress the model. CPU offloading might be necessary, but this will significantly degrade performance. Distributed inference across multiple GPUs is another option, but it requires a more complex setup. If performance is critical, consider using a GPU with more VRAM, such as an RTX 6000 Ada Generation or an A100, or utilizing cloud-based GPU resources.