The NVIDIA A100 40GB, while a powerful GPU, falls short when attempting to run Mistral Large 2 due to insufficient VRAM. Mistral Large 2, with its 123 billion parameters, requires approximately 246GB of VRAM when using FP16 precision. The A100 40GB only provides 40GB of VRAM, leaving a significant deficit of 206GB. This discrepancy prevents the model from being loaded entirely onto the GPU, leading to out-of-memory errors and preventing successful inference. While the A100 boasts a high memory bandwidth of 1.56 TB/s, this bandwidth cannot compensate for the lack of sufficient on-device memory to hold the model.
Even with techniques like model parallelism (splitting the model across multiple GPUs), the single A100 40GB cannot handle the model's memory footprint. The high number of CUDA and Tensor cores are rendered ineffective as the model cannot be fully loaded. Attempting to run the model in its native FP16 precision will undoubtedly fail. Therefore, without significant optimization or offloading techniques, the A100 40GB is unsuitable for running Mistral Large 2.
Due to the severe VRAM limitations, running Mistral Large 2 directly on the NVIDIA A100 40GB is not feasible without significant compromises. Consider using quantization techniques such as 4-bit or 8-bit quantization to drastically reduce the model's memory footprint. Frameworks like `llama.cpp` or `text-generation-inference` are optimized for quantized models and can help manage memory efficiently. Alternatively, explore cloud-based solutions that offer access to GPUs with higher VRAM capacities, such as A100 80GB or H100 GPUs. Model parallelism across multiple GPUs is another option, but it requires significant infrastructure and expertise to implement effectively.
If you choose to proceed with the A100 40GB, focus on aggressive quantization and offloading layers to system RAM (CPU). Be prepared for extremely slow inference speeds and limited batch sizes. Carefully manage context length to minimize memory usage. Ultimately, upgrading to a GPU with more VRAM or utilizing cloud-based resources is the most practical solution for running Mistral Large 2.