The NVIDIA RTX A5000, with its 24GB of GDDR6 VRAM, falls significantly short of the 246GB VRAM demanded by Mistral Large 2 when running in FP16 (half-precision floating point). This colossal difference of 222GB means the entire model cannot be loaded onto the GPU at once. The A5000's 770 GB/s memory bandwidth, while substantial, becomes a bottleneck if offloading model layers to system RAM is attempted. This is because transferring data between the GPU and system RAM is much slower than accessing VRAM directly, drastically reducing inference speed. Furthermore, even if aggressive quantization techniques are applied, fitting the model entirely into the A5000's VRAM remains highly improbable, resulting in extremely slow or non-functional performance.
Due to the substantial VRAM disparity, directly running Mistral Large 2 on a single RTX A5000 is not feasible. Consider exploring distributed inference across multiple GPUs with sufficient combined VRAM. Alternatively, investigate more aggressive quantization methods, such as 4-bit or even 2-bit quantization, although this will come at the cost of reduced accuracy. Cloud-based inference services, which offer access to more powerful GPUs or distributed setups, are another viable option. Finally, consider using a smaller, less demanding model that can fit within the A5000's VRAM, such as Mistral 7B or a quantized version of Llama 2.