The NVIDIA RTX 3060 Ti, with its 8GB of GDDR6 VRAM, falls significantly short of the VRAM requirements for running Llama 3.3 70B. This model, in FP16 (half-precision floating point) format, demands approximately 140GB of VRAM. The deficit of 132GB means the entire model cannot be loaded onto the GPU for inference. While the RTX 3060 Ti boasts 4864 CUDA cores and 152 Tensor cores based on the Ampere architecture, enabling faster computations when the model *can* fit, the VRAM limitation is a hard constraint.
Even if techniques like CPU offloading or splitting the model across multiple GPUs were considered, the memory bandwidth of 0.45 TB/s on the RTX 3060 Ti would become a bottleneck. Transferring data between the CPU and GPU or between GPUs would introduce significant latency, severely impacting the tokens/second generation rate. The large context length of 128,000 tokens further exacerbates the VRAM demand during inference, making it impossible to execute the model without substantial modifications.
Running Llama 3.3 70B directly on an RTX 3060 Ti is not feasible due to the VRAM limitations. Instead, consider using a smaller language model that fits within the GPU's memory capacity. If using Llama 3 is essential, explore cloud-based inference services or platforms like Google Colab Pro+ or cloud GPU instances with sufficient VRAM (e.g., A100, H100). Alternatively, investigate model quantization techniques like 4-bit or even lower precision quantization, but be aware that this will lead to a degradation in the model's quality. Offloading layers to system RAM might be possible, but will drastically slow down the inference speed.