The NVIDIA RTX 3090 Ti, while a powerful GPU, falls short when attempting to run the Mixtral 8x22B (141.00B) model due to insufficient VRAM. Mixtral 8x22B in FP16 precision requires approximately 282GB of VRAM to load the entire model and its associated data. The RTX 3090 Ti is equipped with only 24GB of VRAM. This means that the model cannot be loaded onto the GPU in its entirety, leading to a 'FAIL' compatibility verdict. The large discrepancy of 258GB (282GB required - 24GB available) makes direct inference impossible without significant modifications.
Beyond VRAM, the memory bandwidth of the RTX 3090 Ti (1.01 TB/s) would likely become a bottleneck even if the model *could* fit in memory. Large language models like Mixtral 8x22B involve frequent memory transfers between the GPU and system RAM (if offloading were used), which can significantly impact performance. The CUDA cores (10752) and Tensor Cores (336) would be utilized if the model was loaded, but given the VRAM limitation, these resources are largely irrelevant in this scenario. Without sufficient VRAM, the model will either fail to load or result in extremely slow performance due to constant swapping of data between the GPU and system memory, rendering it practically unusable.
Given the significant VRAM deficit, directly running Mixtral 8x22B on a single RTX 3090 Ti is not feasible. To achieve any level of usability, you would need to consider extreme quantization techniques (e.g., 4-bit or even lower) in conjunction with aggressive offloading strategies, such as splitting the model across multiple GPUs or leveraging system RAM. However, even with these techniques, performance will be significantly degraded, likely resulting in very low tokens per second.
Alternatively, consider using cloud-based inference services or platforms that offer access to GPUs with sufficient VRAM, such as NVIDIA A100 or H100. Another option is to explore smaller, more manageable models that fit within the 24GB VRAM of the RTX 3090 Ti. Fine-tuning a smaller model on a relevant dataset can often provide comparable performance for specific tasks without the massive VRAM requirements of models like Mixtral 8x22B. Distributed inference across multiple RTX 3090 Ti GPUs using frameworks like DeepSpeed is a possibility, but requires significant technical expertise and infrastructure.