The NVIDIA RTX 6000 Ada, while a powerful GPU, falls short of the VRAM requirements for running Mistral Large 2 in FP16 (half-precision floating point). Mistral Large 2, with its 123 billion parameters, demands approximately 246GB of VRAM when using FP16 precision. The RTX 6000 Ada only provides 48GB of VRAM, resulting in a significant VRAM deficit of 198GB. This means the entire model cannot be loaded onto the GPU simultaneously, leading to out-of-memory errors. Furthermore, even if techniques like offloading some layers to system RAM were employed, the performance would be severely impacted due to the much slower transfer speeds between system RAM and the GPU compared to VRAM, negating the benefits of the RTX 6000 Ada's powerful CUDA and Tensor cores.
The memory bandwidth of the RTX 6000 Ada, at 0.96 TB/s, is substantial but irrelevant in this scenario because the primary bottleneck is the insufficient VRAM. Even with high memory bandwidth, the GPU can't process data it can't access. The large context length of Mistral Large 2 (128,000 tokens) further exacerbates the VRAM issue, as processing longer sequences requires more memory for storing intermediate activations and attention weights. Consequently, running Mistral Large 2 on the RTX 6000 Ada without significant modifications or workarounds is not feasible.
Given the VRAM limitations, directly running Mistral Large 2 on the RTX 6000 Ada is impractical. Consider using quantization techniques like 4-bit or 8-bit to significantly reduce the model's memory footprint. This might allow the model to fit within the available VRAM, but will likely result in a noticeable reduction in output quality. Alternatively, explore cloud-based inference services or renting GPUs with sufficient VRAM (e.g., A100, H100) to run Mistral Large 2 without compromising performance. For local execution, investigate model parallelism, where the model is split across multiple GPUs, although this requires significant engineering effort and specialized software support.
If you choose to attempt running a quantized version locally, prioritize using an inference framework optimized for low-VRAM scenarios, like `llama.cpp` or `exllamav2`. Be prepared to experiment extensively with different quantization levels and batch sizes to find a configuration that balances performance and memory usage. Monitor GPU utilization and memory consumption closely to avoid out-of-memory errors. Note that even with aggressive quantization, the performance will likely be significantly slower than on a GPU with adequate VRAM.