The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and Ampere architecture, is a powerful GPU, but it falls short of the VRAM requirements for running Llama 3 70B even with quantization. While the card boasts a high memory bandwidth of 1.01 TB/s and a substantial number of CUDA and Tensor cores (10752 and 336 respectively), the sheer size of the 70 billion parameter model necessitates more VRAM. The provided Q3_K_M quantization brings the VRAM footprint down to 28GB, which is still 4GB over the 3090 Ti's capacity. This VRAM deficit will prevent the model from loading and running effectively, leading to out-of-memory errors. The architecture itself would be capable enough to handle the compute load if the model could fit in memory.
Given the VRAM limitation, running Llama 3 70B on a single RTX 3090 Ti is not feasible. Several options exist: First, consider using a smaller model variant of Llama 3, such as the 8B or 7B versions, which are designed to fit within smaller memory footprints. These smaller models will sacrifice some performance and accuracy but can run without modification. Second, investigate model parallelism across multiple GPUs. This involves splitting the model across several GPUs, each holding a portion of the parameters. This requires more complex software setup and is not always straightforward. Finally, explore offloading some layers to system RAM (CPU), which is slower but allows you to run larger models. This will dramatically decrease inference speed.