The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, 10496 CUDA cores, and 0.94 TB/s memory bandwidth, provides ample resources for running the Llama 3.1 8B model, especially when utilizing quantization. The Q4_K_M quantization reduces the model's VRAM footprint to approximately 4GB. This leaves a significant 20GB VRAM headroom, ensuring that the model and its associated processes can operate without memory constraints. The RTX 3090's Ampere architecture and Tensor Cores are well-suited for accelerating the matrix multiplications inherent in transformer models like Llama 3.1, contributing to efficient inference.
Given the substantial VRAM headroom, users can experiment with larger batch sizes or context lengths to optimize throughput. While Q4_K_M provides a good balance between size and performance, consider experimenting with unquantized (FP16) or higher bit quantization if the application demands maximum accuracy and the available VRAM allows. Monitor GPU utilization during inference to identify potential bottlenecks; if the GPU is not fully utilized, increasing the batch size or context length might improve performance. For optimal throughput, explore using inference-optimized frameworks like `vLLM` or `text-generation-inference`.