The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, falls short of the VRAM needed to efficiently run the Q4_K_M quantized version of the Llama 3 70B model, which requires approximately 35GB of VRAM. This 11GB deficit will lead to out-of-memory errors, severely degraded performance due to offloading to system RAM (if possible at all), or complete inability to run the model. While the RTX 3090 boasts a high memory bandwidth of 0.94 TB/s and a substantial number of CUDA and Tensor cores, the limiting factor is the insufficient VRAM to hold the entire model in GPU memory. The Ampere architecture is capable, but VRAM capacity is paramount for large language models like Llama 3 70B.
Given the VRAM limitations, directly running the Llama 3 70B model on a single RTX 3090 is not feasible. To run this model, consider upgrading to a GPU with significantly more VRAM (48GB or more). Alternatively, explore model parallelism across multiple GPUs, although this requires more complex setup and specialized software. As a more immediate solution, try a more aggressive quantization of the model such as Q2_K. Be aware that aggressive quantization will affect the quality of the model's output. Consider using cloud-based GPU services or renting time on a more powerful machine if local hardware upgrades are not an option.