The NVIDIA A100 80GB, while a powerful GPU, falls short of the VRAM requirements for running Mistral Large 2 in FP16 precision. Mistral Large 2, with its 123 billion parameters, demands approximately 246GB of VRAM when using FP16 (half-precision floating point). The A100 80GB provides only 80GB of VRAM, resulting in a significant deficit of 166GB. This VRAM limitation prevents the model from being loaded entirely onto the GPU, leading to out-of-memory errors and the inability to perform inference directly.
While the A100 boasts a high memory bandwidth of 2.0 TB/s and a substantial number of CUDA and Tensor cores, these advantages cannot compensate for the insufficient VRAM. Memory bandwidth is crucial for transferring data between the GPU and its memory, and the A100 excels in this aspect. However, if the model cannot fit into the available VRAM, the high bandwidth becomes irrelevant. Similarly, the CUDA and Tensor cores, designed for parallel processing and accelerating AI workloads, remain underutilized due to the VRAM constraint. Without adequate VRAM, the A100 cannot leverage its computational power effectively for Mistral Large 2.
To run Mistral Large 2 on the NVIDIA A100 80GB, you'll need to employ techniques to reduce the VRAM footprint. Quantization is a crucial optimization strategy. Consider using 4-bit quantization (bitsandbytes or similar) or even lower precision formats like 2-bit quantization if the accuracy loss is acceptable for your application. Model parallelism, where the model is split across multiple GPUs, is another option, but it requires a multi-GPU setup. CPU offloading could be used as a last resort but it will significantly reduce inference speed.
If performance is critical, explore alternative models with smaller parameter sizes or consider upgrading to a GPU with more VRAM, such as an NVIDIA H100 or A100 with more memory, or using a multi-GPU setup. Cloud-based inference services are also a viable option, as they often provide access to high-VRAM GPUs and optimized inference infrastructure. Always prioritize testing different configurations to find the optimal balance between performance and accuracy for your specific use case.