The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is a powerful GPU, but it falls short of the VRAM requirements for running Llama 3.1 70B (70.00B) even in its quantized q3_k_m format. This quantization reduces the model's footprint to approximately 28GB, exceeding the 3090 Ti's available 24GB by 4GB. While the Ampere architecture and its 10752 CUDA cores and 336 Tensor cores are well-suited for AI inference, the VRAM limitation is a hard constraint. Running out of VRAM will lead to errors, crashes, or extremely slow performance as the system resorts to swapping memory between the GPU and system RAM, which is significantly slower.
Unfortunately, running Llama 3.1 70B (70.00B) in q3_k_m on a single RTX 3090 Ti is not feasible due to insufficient VRAM. Consider using a more aggressive quantization method, such as Q2_K or even Q1_K, if available, although this will come at a cost of reduced accuracy. Alternatively, explore distributed inference solutions that split the model across multiple GPUs or even multiple machines. Another option is to use a smaller model, such as Llama 3.1 8B, which will fit within the 3090 Ti's VRAM. Cloud-based inference services offer another viable alternative, allowing you to run the model without hardware constraints, albeit at a cost per use.