The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, provides ample memory to comfortably run the Llama 3.1 8B model, which requires approximately 16GB of VRAM when using FP16 precision. This leaves a significant 8GB VRAM headroom, allowing for larger batch sizes, longer context lengths, and the potential to load additional models or perform other GPU-intensive tasks concurrently. The RTX 3090 Ti's high memory bandwidth of 1.01 TB/s is also crucial, ensuring fast data transfer between the GPU and memory, which directly impacts inference speed and overall performance. The Ampere architecture, with its 10752 CUDA cores and 336 Tensor cores, further accelerates computations, particularly matrix multiplications that are fundamental to deep learning.
The RTX 3090 Ti is an excellent choice for running Llama 3.1 8B. To maximize performance, start with a batch size of 5 and explore increasing it until you observe diminishing returns or encounter memory limitations. Experiment with different context lengths, keeping in mind the model's maximum of 128000 tokens. Consider using quantization techniques like Q4 or Q5 to further reduce memory footprint and potentially increase inference speed, although this might come with a slight reduction in accuracy. Monitoring GPU utilization and memory usage is recommended to fine-tune settings for optimal performance.