The NVIDIA RTX 4070 Ti, with its 12GB of GDDR6X VRAM, falls significantly short of the VRAM requirements for running Llama 3.3 70B in FP16 (140GB). This discrepancy means the entire model cannot be loaded onto the GPU, making direct inference impossible without significant modifications. While the RTX 4070 Ti boasts a memory bandwidth of 0.5 TB/s and 7680 CUDA cores, these specifications become irrelevant when the primary bottleneck is VRAM capacity. Attempting to load the model will likely result in out-of-memory errors, preventing any meaningful computation.
Even if techniques like CPU offloading were employed, the substantial data transfer between the GPU and system RAM would severely limit performance. The relatively high TDP of 285W suggests the card is designed for demanding workloads, but its VRAM limitation is a critical constraint for large language models like Llama 3.3 70B. The Ada Lovelace architecture provides advancements in tensor core performance, which could be beneficial with quantization, but the VRAM bottleneck overshadows these advantages.
To run Llama 3.3 70B on a system with an RTX 4070 Ti, aggressive quantization is essential. Consider using 4-bit quantization (Q4_K_M or similar) via llama.cpp or similar frameworks. This reduces the VRAM footprint drastically, potentially bringing it within the 4070 Ti's capacity. However, expect a noticeable performance decrease compared to running the model in FP16 or even 8-bit quantization. Alternatively, explore cloud-based inference services or distributed computing solutions if performance is critical and local execution is a must.
Another option is to use CPU offloading in conjunction with quantization, but this will significantly slow down inference speed due to the limited bandwidth between the GPU and CPU. If feasible, consider upgrading to a GPU with significantly more VRAM (24GB or more) for a better experience with large language models.