The NVIDIA RTX 4070 Ti SUPER, while a capable card for many AI tasks, falls short when trying to run Llama 3.3 70B directly due to insufficient VRAM. Llama 3.3 70B in FP16 precision requires approximately 140GB of VRAM to load the model weights and perform inference. The RTX 4070 Ti SUPER only provides 16GB of GDDR6X memory. This massive discrepancy of 124GB means the model cannot be loaded entirely onto the GPU, leading to a failure in compatibility. Memory bandwidth, while respectable at 0.67 TB/s, is secondary to the primary limitation of VRAM. Even with high memory bandwidth, the GPU cannot process data it doesn't have access to.
To run Llama 3.3 70B with the RTX 4070 Ti SUPER, you'll need to offload some of the model layers to system RAM or explore aggressive quantization techniques. Consider using `llama.cpp` with Q4_K_M or even lower quantization levels. This will significantly reduce the VRAM footprint, potentially making the model runnable, albeit with a performance trade-off. Alternatively, explore cloud-based solutions or distributed inference setups with multiple GPUs if you need to run the model at higher precision and speed. Model parallelism across multiple GPUs is a viable, albeit more complex, option.