The primary limiting factor for running large language models like Llama 3.1 70B is VRAM. This model, in FP16 precision, requires approximately 140GB of VRAM to load and operate effectively. The NVIDIA RTX 3090, while a powerful card, only offers 24GB of VRAM. This results in a significant VRAM deficit of 116GB. Without sufficient VRAM, the model cannot be fully loaded onto the GPU, leading to errors or preventing execution altogether. Even if offloading some layers to system RAM was attempted, the performance would be severely degraded due to the much slower transfer speeds compared to the GPU's GDDR6X memory. The memory bandwidth of the RTX 3090 (0.94 TB/s) would also be underutilized in such a scenario, further impacting the inference speed.
Given the VRAM limitations, directly running Llama 3.1 70B on an RTX 3090 in FP16 is not feasible. To work around this, consider using quantization techniques like 4-bit or 8-bit quantization (Q4/Q8). This reduces the memory footprint of the model, potentially bringing it within the RTX 3090's VRAM capacity. Another approach is to explore offloading some layers to the CPU, though this will significantly impact performance. Alternatively, consider using cloud-based inference services or upgrading to a GPU with more VRAM, such as an NVIDIA A100 or H100, if possible. Distributed inference across multiple GPUs is another viable solution, though it requires more complex setup and infrastructure.