The NVIDIA RTX 4060 Ti 8GB is fundamentally incompatible with running the Llama 3.3 70B model due to insufficient VRAM. Llama 3.3 70B in FP16 precision requires approximately 140GB of VRAM to load the model weights and intermediate activations. The RTX 4060 Ti 8GB only provides 8GB of VRAM, resulting in a massive VRAM deficit of 132GB. This means the model cannot be loaded onto the GPU in its native FP16 format.
Even with aggressive quantization techniques, the model's memory footprint will likely remain significantly larger than the available VRAM. While quantization can reduce the size of the model, the 70B parameter count is simply too large for the 8GB VRAM capacity of the RTX 4060 Ti. The memory bandwidth of 0.29 TB/s, while decent for gaming, will be a bottleneck even if the model could somehow fit into the limited VRAM, resulting in extremely slow inference speeds. The 4352 CUDA cores and 136 Tensor cores, while helpful, cannot overcome the fundamental limitation imposed by the lack of VRAM.
Due to the severe VRAM limitation, running Llama 3.3 70B directly on the RTX 4060 Ti 8GB is not feasible. Consider using cloud-based inference services like NelsaHost, Google Colab Pro, or RunPod, which offer GPUs with significantly more VRAM. Alternatively, you could explore model distillation techniques to create a smaller, more manageable model that can run on your hardware, although this would come at the cost of accuracy. Another option is to offload layers to system RAM, but this will result in extremely slow performance and is generally not recommended for interactive use.