The NVIDIA RTX 3080 Ti, with its 12GB of GDDR6X VRAM, falls significantly short of the 140GB VRAM requirement for running the Llama 3.3 70B model in FP16 precision. This discrepancy of -128GB of VRAM headroom means the model, in its full FP16 form, cannot be loaded onto the GPU for inference. Memory bandwidth, while substantial at 0.91 TB/s, becomes irrelevant if the model cannot fit into the available VRAM. The Ampere architecture and its CUDA/Tensor cores are powerful, but they cannot compensate for the fundamental lack of memory capacity. Without sufficient VRAM, the model will likely crash or produce errors due to out-of-memory issues.
Given the substantial VRAM deficit, running Llama 3.3 70B directly on the RTX 3080 Ti is impractical without aggressive quantization. Consider using quantization techniques like 4-bit or even 3-bit quantization (using libraries like `llama.cpp`) to significantly reduce the model's memory footprint. Alternatively, explore offloading layers to system RAM, though this will drastically reduce inference speed. If high performance is a priority, consider using cloud-based GPU instances with sufficient VRAM or distributing the model across multiple GPUs using frameworks designed for model parallelism. For local usage, consider smaller models like Llama 3.3 8B or Mistral 7B, which can operate within the 3080 Ti's VRAM constraints.