The NVIDIA RTX 4080 SUPER, with its 16GB of GDDR6X VRAM, falls significantly short of the 140GB VRAM required to load the Llama 3.3 70B model in FP16 precision. This discrepancy of 124GB means the model, in its full FP16 form, cannot reside entirely within the GPU's memory. Furthermore, even if techniques like offloading were employed, the limited 740 GB/s memory bandwidth of the RTX 4080 SUPER would become a bottleneck. Constant data transfer between system RAM and the GPU would drastically reduce inference speed, making real-time or interactive applications impractical. The 10240 CUDA cores and 320 Tensor cores, while powerful, cannot compensate for the fundamental memory constraint.
To run Llama 3.3 70B on an RTX 4080 SUPER, aggressive quantization is essential. Consider using Q4_K_M or even lower quantization levels like Q2_K to significantly reduce the model's memory footprint. Utilizing a framework like `llama.cpp` is highly recommended as it's optimized for CPU+GPU inference and supports various quantization methods. Even with quantization, expect significantly reduced performance compared to GPUs with larger VRAM capacities. Explore cloud-based solutions or consider renting time on more powerful GPUs if higher performance is crucial. Alternatively, investigate smaller models within the Llama 3 family, such as the 8B parameter model, which might be more suitable for the 4080 SUPER's memory constraints.