The primary limiting factor when running large language models like Llama 3.3 70B is VRAM (Video RAM). Llama 3.3 70B in FP16 (half-precision floating point) requires approximately 140GB of VRAM to load the model weights and perform inference. The NVIDIA RTX 3090, while a powerful GPU, only has 24GB of VRAM. This means the model, in its full FP16 precision, cannot fit entirely within the GPU's memory, leading to an 'out-of-memory' error. Memory bandwidth, while important for performance, is secondary to VRAM capacity in this scenario. The RTX 3090's 0.94 TB/s memory bandwidth would be sufficient *if* the model fit into VRAM.
Without sufficient VRAM, the model cannot be loaded for inference. Even if techniques like CPU offloading are attempted, the performance would be severely degraded due to the slow data transfer rates between the CPU and GPU. The number of CUDA cores and Tensor cores are also rendered largely irrelevant because the bottleneck is the inability to load the model into the GPU in the first place. Consequently, estimations for tokens/sec and batch size are not feasible under these circumstances.
To run Llama 3.3 70B on an RTX 3090, you'll need to significantly reduce the model's memory footprint. This can be achieved through quantization. Consider using a quantization method like 4-bit or 8-bit quantization. This will reduce the VRAM requirement, potentially bringing it within the RTX 3090's 24GB capacity. Frameworks like `llama.cpp` and `vLLM` are excellent choices for implementing quantization and optimizing inference.
Another option, although less desirable due to performance implications, is to offload some layers of the model to the CPU. However, this will result in a significant performance decrease. If feasible, consider using cloud-based GPU instances with higher VRAM capacity, such as those offered by NelsaHost, for optimal performance with Llama 3.3 70B.