The NVIDIA RTX 4090, while a powerful GPU, possesses 24GB of VRAM. Running Llama 3.1 70B (70.00B) in FP16 (full precision) requires approximately 140GB of VRAM. This means the RTX 4090 falls significantly short, lacking 116GB of the necessary memory. The model's parameters, totaling 70 billion, are simply too large to fit within the GPU's memory footprint in its native FP16 format. Memory bandwidth, while substantial at 1.01 TB/s, becomes irrelevant when the entire model cannot be loaded onto the GPU. Attempting to run this model without sufficient VRAM will result in out-of-memory errors, preventing successful inference.
Due to the VRAM limitations of the RTX 4090, running Llama 3.1 70B (70.00B) in its full FP16 precision is not feasible. To make it work, you must employ aggressive quantization techniques. Quantization reduces the memory footprint of the model by representing weights with fewer bits. Consider using a framework like `llama.cpp` or `vLLM` to leverage quantization down to 4-bit or even 2-bit. Another option is to explore offloading some layers to system RAM, but this will significantly impact performance. Alternatively, consider using a smaller model or splitting the model across multiple GPUs if possible. Finally, cloud-based inference services with access to larger GPUs are a viable alternative.