The NVIDIA RTX 3090 Ti, while a powerful GPU with 10752 CUDA cores and 24GB of GDDR6X VRAM, falls short of the VRAM requirements for running the Mixtral 8x7B model, even when quantized to INT8. Mixtral 8x7B, a sparse mixture-of-experts model, demands substantial memory resources. In its INT8 quantized form, the model requires approximately 46.7GB of VRAM to load and operate. The RTX 3090 Ti's 24GB VRAM capacity leaves a deficit of 22.7GB, preventing the model from being loaded entirely onto the GPU. This limitation will cause out-of-memory errors, making direct inference impossible without employing techniques to reduce the memory footprint or offload parts of the model to system RAM, which significantly impacts performance.
Even with the RTX 3090 Ti's impressive memory bandwidth of 1.01 TB/s, the bottleneck is the insufficient VRAM. While high memory bandwidth is crucial for quickly transferring data between the GPU and its memory, it cannot compensate for the lack of capacity to hold the entire model. Running the model with insufficient VRAM would necessitate offloading layers to system RAM, which is significantly slower than GDDR6X, leading to a substantial performance decrease. The Ampere architecture's Tensor Cores would be underutilized, as the model cannot fully reside on the GPU for efficient computation. Therefore, while the RTX 3090 Ti possesses capable hardware, it's fundamentally limited by its VRAM capacity for this specific model.
Due to the VRAM limitations, directly running Mixtral 8x7B on the RTX 3090 Ti is not feasible. Consider using CPU offloading or splitting the model across multiple GPUs if available. If offloading to CPU, expect a significant performance hit. Another approach is to explore more aggressive quantization methods, such as Q4 or even lower, which can substantially reduce the VRAM footprint, although this will come at the cost of some accuracy. Model distillation is another advanced technique where a smaller model is trained to mimic the behavior of the larger Mixtral model, making it suitable for the RTX 3090 Ti.
Alternatively, consider using cloud-based GPU instances with higher VRAM capacities, such as those offered by NelsaHost, to run the model without these limitations. If sticking with local hardware, investigate using inference frameworks that support advanced memory management techniques like swapping layers between GPU and system memory, but be aware that this will severely impact inference speed. Prioritize minimizing context length to reduce memory usage as well.