The primary limiting factor for running large language models (LLMs) like Llama 3.1 70B on consumer GPUs is VRAM. This model, even with aggressive Q3_K_M quantization, requires 28GB of VRAM. The RTX 3090, while a powerful card, only offers 24GB. This 4GB deficit means the model's weights, activations, and intermediate calculations cannot fully reside on the GPU simultaneously, leading to out-of-memory errors or reliance on system RAM, which is significantly slower. The RTX 3090's 0.94 TB/s memory bandwidth is excellent, but it can't compensate for insufficient VRAM. The Ampere architecture and its tensor cores would otherwise provide decent acceleration for matrix multiplications, but the VRAM bottleneck prevents their full utilization.
Even if you could *technically* get the model running by offloading layers to system RAM or using techniques like CPU offloading, the performance would be severely degraded. The constant swapping of data between the GPU and system RAM would introduce significant latency, resulting in very low tokens/second generation speed, making interactive use impractical. The large context length of 128000 tokens also exacerbates the VRAM issue, as longer contexts require more memory for attention mechanisms and key-value caches. The 10496 CUDA cores and 328 Tensor cores on the RTX 3090 are underutilized in this scenario.
Due to the VRAM limitations, running Llama 3.1 70B with Q3_K_M quantization on a single RTX 3090 is not feasible for practical use. Consider using a smaller model, such as a 13B or 34B parameter variant of Llama 3, which can fit within the RTX 3090's VRAM. Alternatively, explore cloud-based solutions or services that offer access to GPUs with sufficient VRAM. If you are set on running the 70B model locally, you would need to investigate methods like model parallelism across multiple GPUs, but this requires significant technical expertise and a suitable multi-GPU setup.