The primary limitation in running Llama 3.3 70B on the NVIDIA RTX 6000 Ada is the VRAM capacity. Llama 3.3 70B, in its FP16 (half-precision floating point) format, requires approximately 140GB of VRAM to load the entire model. The RTX 6000 Ada provides only 48GB of VRAM. This creates a significant shortfall of 92GB, preventing the model from being loaded and executed directly. While the RTX 6000 Ada boasts a high memory bandwidth of 0.96 TB/s and a substantial number of CUDA and Tensor cores, these specifications become irrelevant if the model cannot fit into the available VRAM. The Ada Lovelace architecture is capable, but memory capacity is the bottleneck here.
Without sufficient VRAM, the system will either fail to load the model entirely or experience extremely poor performance due to constant swapping of model weights between the system RAM and the GPU VRAM. This swapping process, known as offloading, severely impacts inference speed, rendering the model practically unusable for real-time applications. The estimated tokens per second and batch size are therefore undefined in this scenario, as performance will be drastically limited by the VRAM constraint.
Given the VRAM limitations, directly running Llama 3.3 70B in FP16 on a single RTX 6000 Ada is not feasible. To make it work, you'll need to consider quantization techniques, such as Q4 or even lower precisions (e.g., Q2, Q3), which significantly reduce the VRAM footprint of the model. QuantizationAwareTraining is a more advanced technique to maintain accuracy after quantization. Even with quantization, performance might be suboptimal compared to running the model on a GPU with sufficient VRAM. Alternatively, consider using a distributed inference setup across multiple GPUs or leveraging cloud-based inference services that offer larger GPU instances. For local execution, explore model parallelism frameworks to split the model across multiple GPUs if available. If you only have a single RTX 6000 Ada, stick to smaller models that fit within the 48GB VRAM.