The primary limiting factor in running large language models (LLMs) like Llama 3.3 70B is VRAM capacity. This model, in its FP16 (half-precision floating point) format, requires approximately 140GB of VRAM to load the model weights and perform inference. The NVIDIA RTX 4090, while a powerful GPU, only offers 24GB of VRAM. This significant shortfall of 116GB means the model cannot be loaded in its entirety onto the GPU for processing, resulting in a compatibility failure. Memory bandwidth, while important for performance, is secondary to the absolute VRAM requirement in this scenario. Even with the RTX 4090's impressive 1.01 TB/s memory bandwidth, it cannot compensate for the lack of sufficient on-board memory to hold the model.
To run Llama 3.3 70B on an RTX 4090, you'll need to employ techniques to reduce the model's memory footprint. Quantization, which involves reducing the precision of the model's weights (e.g., to 4-bit or 8-bit), is essential. Consider using llama.cpp or similar frameworks that support aggressive quantization. Even with quantization, achieving acceptable performance may require offloading some layers to system RAM. This will significantly reduce inference speed, but it may be the only way to run the model. Alternatively, consider using cloud-based inference services or platforms with GPUs that have larger VRAM capacities, such as the NVIDIA A100 or H100.