The NVIDIA RTX 3090, equipped with 24GB of GDDR6X VRAM, falls short of the 28GB VRAM requirement for running Llama 3 70B (70.00B) using the q3_k_m quantization method. This discrepancy of 4GB means the model, even in its quantized form, cannot fully reside on the GPU's memory. Consequently, the system will either fail to load the model or experience severe performance degradation due to constant swapping of data between the GPU and system RAM. While the RTX 3090 boasts a high memory bandwidth of 0.94 TB/s and a substantial number of CUDA and Tensor cores, these specifications are rendered less effective when the model exceeds the available VRAM, creating a bottleneck that significantly impacts inference speed and overall usability.
To successfully run Llama 3 70B (70.00B) on the RTX 3090, consider further quantization to reduce the VRAM footprint. Quantization methods like Q2_K or even Q1_K could potentially bring the model's VRAM requirements within the 24GB limit. Alternatively, explore offloading some layers to the CPU, although this will significantly reduce inference speed. If performance remains unsatisfactory, consider using a smaller model like Llama 3 8B, which is designed to run efficiently on GPUs with less VRAM, or upgrading to a GPU with more VRAM.