The primary limiting factor when running large language models like Llama 3.3 70B 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 3090 Ti, while a powerful GPU, only offers 24GB of VRAM. This results in a significant shortfall of 116GB, making it impossible to load the entire model onto the GPU for inference in FP16. While the RTX 3090 Ti boasts a high memory bandwidth of 1.01 TB/s and a substantial number of CUDA and Tensor cores, these specifications become irrelevant when the model cannot fit into the available memory.
Even with techniques like CPU offloading (moving some model layers to system RAM), the performance would be severely degraded due to the much slower data transfer rates between the system RAM and the GPU. The memory bandwidth of system RAM is significantly lower than the 1.01 TB/s offered by the RTX 3090 Ti's GDDR6X memory. This bottleneck would result in extremely slow inference speeds, rendering the model practically unusable. Without sufficient VRAM, the RTX 3090 Ti cannot effectively leverage its computational power for Llama 3.3 70B.
Given the VRAM limitations, running Llama 3.3 70B directly on the RTX 3090 Ti is not feasible without significant compromises. Consider using quantization techniques like 4-bit or 8-bit quantization to reduce the model's memory footprint. This can be achieved using frameworks like `llama.cpp` or `vLLM`. Even with quantization, performance will be limited, and you may need to experiment with smaller batch sizes and shorter context lengths. Alternatively, explore cloud-based solutions or renting a GPU with sufficient VRAM (e.g., an NVIDIA A100 or H100 with 80GB or more VRAM) to achieve acceptable performance. Distributed inference across multiple GPUs is another option, but it requires significant technical expertise and infrastructure.