The NVIDIA RTX 4060 Ti 16GB is not directly compatible with the Mistral Large 2 model due to a significant VRAM discrepancy. Mistral Large 2, with its 123 billion parameters, requires approximately 246GB of VRAM when using FP16 (half-precision floating point) for storing the model weights and activations during inference. The RTX 4060 Ti 16GB only provides 16GB of VRAM. This 230GB VRAM shortfall means the entire model cannot reside on the GPU's memory simultaneously, leading to out-of-memory errors. While the RTX 4060 Ti leverages the Ada Lovelace architecture and has 4352 CUDA cores and 136 Tensor cores, these computational resources are rendered largely ineffective because the model can't be fully loaded. The memory bandwidth of 0.29 TB/s, although decent, will be a bottleneck if offloading to system RAM is attempted, drastically reducing inference speed.
Directly running Mistral Large 2 on the RTX 4060 Ti 16GB is impractical without substantial modifications. Instead of running it directly, consider using cloud-based inference services or explore model quantization techniques like 4-bit or even lower precision to drastically reduce the model's memory footprint. Alternatively, investigate methods like CPU offloading, where parts of the model are processed on the system's RAM, but be aware that this will significantly reduce inference speed. For local experimentation, consider smaller models that fit within the 16GB VRAM limit or explore distributed inference across multiple GPUs if feasible.