The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, boasts impressive specifications for a consumer-grade GPU, including 10752 CUDA cores and a memory bandwidth of 1.01 TB/s. However, running large language models (LLMs) like Llama 3 70B presents a significant challenge due to its substantial memory footprint. Llama 3 70B, with 70 billion parameters, requires approximately 140GB of VRAM when using FP16 (half-precision floating point) for model weights. This is a common precision for balancing speed and accuracy during inference.
The incompatibility arises because the 3090 Ti's 24GB VRAM is far below the 140GB needed to load the entire Llama 3 70B model in FP16. This creates a VRAM deficit of 116GB. Without sufficient VRAM, the model cannot be fully loaded onto the GPU, preventing successful inference. Attempting to run the model in this configuration will likely result in out-of-memory errors. Even with its high memory bandwidth, the 3090 Ti simply lacks the capacity to hold the model's parameters, precluding any meaningful performance evaluation or token generation.
Given the VRAM limitations, running Llama 3 70B on a single RTX 3090 Ti is not feasible without significant modifications. The primary recommendation is to explore quantization techniques. Quantization reduces the precision of the model's weights, thereby decreasing the VRAM footprint. For example, using 4-bit quantization (Q4) could potentially bring the VRAM requirement down to a manageable level. Alternatively, consider using offloading techniques, where parts of the model are stored in system RAM and swapped in and out of the GPU as needed. However, this will severely impact performance due to the slower transfer speeds between system RAM and GPU VRAM.
Another viable option is to leverage distributed inference across multiple GPUs, if available. Frameworks like vLLM and PyTorch's `torch.distributed` support model parallelism, allowing you to split the model across multiple GPUs, each holding a portion of the model's parameters. If neither quantization nor multi-GPU inference is possible, consider using a smaller model variant of Llama 3 or exploring cloud-based GPU solutions that offer instances with sufficient VRAM.