The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM, can technically run the Q4_K_M quantized Mixtral 8x7B model, which requires approximately 23.4GB of VRAM. This leaves a very small headroom of only 0.6GB. While the model fits within the GPU's memory, this limited margin can lead to performance bottlenecks and potential out-of-memory errors, especially when dealing with larger context lengths or more complex prompts. The RTX 4090's high memory bandwidth (1.01 TB/s) helps mitigate some of these issues by enabling faster data transfer between the GPU and memory, but the relatively small VRAM headroom remains a primary constraint.
The Ada Lovelace architecture of the RTX 4090, with its 16384 CUDA cores and 512 Tensor cores, is well-suited for accelerating the matrix multiplications and other computations involved in running large language models. However, the model's size and the tight VRAM constraints mean that performance will likely be sub-optimal. Expect relatively low tokens per second (around 16 in this specific scenario) and a small batch size (1), which limits the ability to process multiple requests simultaneously. The model will be running on the GPU, but with significant limitations.
Given the marginal VRAM headroom, consider using a more aggressive quantization method, such as Q3_K_M or even Q2_K. While this will slightly reduce the model's accuracy, it will significantly decrease VRAM usage and improve performance. Experiment with different inference frameworks like llama.cpp or vLLM, as they offer varying levels of optimization and memory management. Monitor VRAM usage closely during inference and reduce the context length if necessary to avoid out-of-memory errors. If performance remains unsatisfactory, consider distributing the model across multiple GPUs or using a cloud-based inference service.