The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is a powerful GPU for many AI tasks. However, running the Llama 3.1 70B model, even with quantization, presents a challenge. The q3_k_m quantization brings the model's VRAM footprint down to 28GB, which still exceeds the RTX 4090's available 24GB. This means the entire model cannot reside on the GPU's memory simultaneously, leading to out-of-memory errors or requiring offloading to system RAM, which significantly degrades performance. The Ada Lovelace architecture and 512 Tensor Cores of the RTX 4090 are well-suited for accelerating matrix multiplications inherent in LLMs, but the limited VRAM becomes the primary bottleneck in this scenario.
Given the VRAM limitation, directly running Llama 3.1 70B quantized to q3_k_m on a single RTX 4090 is not feasible without significant performance compromises. Consider using a more aggressive quantization method, such as q2_k or even lower, if acceptable accuracy loss is tolerated. Alternatively, explore techniques like CPU offloading, but be aware that this will substantially reduce inference speed. For optimal performance, consider upgrading to a GPU with more VRAM (48GB or more) or distributing the model across multiple GPUs using model parallelism.