The primary limiting factor in running large language models (LLMs) like Llama 3.3 70B on consumer GPUs is VRAM. Llama 3.3 70B, in FP16 (half-precision floating point) format, requires approximately 140GB of VRAM to load the model weights. The NVIDIA RTX 3060 12GB, as the name suggests, only provides 12GB of VRAM. This substantial difference of 128GB means the entire model cannot be loaded onto the GPU, making direct inference impossible without significant modifications. The RTX 3060's memory bandwidth of 0.36 TB/s, while adequate for many tasks, would also become a bottleneck if offloading to system RAM were attempted, severely impacting performance.
Beyond VRAM limitations, the computational capabilities of the RTX 3060, specifically its 3584 CUDA cores and 112 Tensor Cores, are also relevant. While these cores can contribute to accelerating matrix multiplications, the sheer scale of the 70B parameter model necessitates a much more powerful GPU with higher core counts and faster memory bandwidth for acceptable inference speeds. Even with aggressive quantization techniques, the limited VRAM remains the dominant constraint. Without fitting the model entirely in GPU memory, performance will be significantly degraded due to constant data transfer between system RAM and the GPU.
Unfortunately, running Llama 3.3 70B directly on an RTX 3060 12GB is not feasible due to the VRAM limitations. Consider exploring cloud-based inference services like NelsaHost, which offer access to GPUs with sufficient VRAM for running large models. Alternatively, investigate techniques like quantization (e.g., using 4-bit or even 2-bit quantization) and offloading layers to CPU RAM. However, expect a significant performance hit with CPU offloading, making it suitable only for experimentation or very low-throughput applications. For local execution, consider smaller models that fit within the RTX 3060's VRAM, or explore distributed inference setups across multiple GPUs if available.