The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and Ampere architecture, is a powerful GPU for many AI tasks. However, running the Llama 3 70B model, even in its Q4_K_M quantized form, presents a significant challenge due to VRAM limitations. The Q4_K_M quantization reduces the model's VRAM footprint to approximately 35GB, but this still exceeds the 3090 Ti's available 24GB by 11GB. This means the entire model cannot reside on the GPU's memory, leading to out-of-memory errors or requiring offloading to system RAM, which drastically reduces performance.
While the 3090 Ti boasts a memory bandwidth of 1.01 TB/s and a substantial number of CUDA and Tensor cores, these strengths are bottlenecked by the insufficient VRAM. When the model exceeds the GPU's memory capacity, data must be constantly swapped between the GPU and system RAM. This data transfer over the PCIe bus is significantly slower than accessing VRAM directly, resulting in a substantial performance degradation. The Ampere architecture's Tensor Cores, designed to accelerate matrix multiplication, will be underutilized due to the constant data swapping.
Unfortunately, directly running the Q4_K_M quantized Llama 3 70B model on a single RTX 3090 Ti is not feasible due to VRAM constraints. Consider exploring smaller models, such as Llama 3 8B, which can fit within the 24GB VRAM. Alternatively, investigate techniques like CPU offloading or model parallelism across multiple GPUs, though these solutions will significantly impact performance. For CPU offloading, llama.cpp offers good CPU support; however, inference speed will be considerably slower compared to GPU-only execution.