The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM, 16384 CUDA cores, and 1.01 TB/s memory bandwidth, is well-suited for running the Mixtral 8x7B (46.70B) model, especially when quantized. The q3_k_m quantization brings the model's VRAM footprint down to 18.7GB, leaving a comfortable 5.3GB VRAM headroom on the RTX 4090. This headroom allows for some flexibility, accommodating larger batch sizes (though limited by memory) or other processes running concurrently on the GPU. The Ada Lovelace architecture's Tensor Cores further accelerate the matrix multiplications crucial for transformer models, leading to faster inference speeds.
For optimal performance with the Mixtral 8x7B model on the RTX 4090, stick with the q3_k_m quantization to ensure the model fits within the available VRAM. Experiment with slightly larger batch sizes, but monitor VRAM usage closely to avoid out-of-memory errors. Consider using `llama.cpp` or `text-generation-inference` for efficient inference. For longer context lengths, be mindful of the increased memory requirements and potential performance impact. Offloading some layers to system RAM can mitigate VRAM limitations, but will significantly reduce performance.