The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM and Ampere architecture, is exceptionally well-suited for running the Llama 3.1 8B model. Llama 3.1 8B in FP16 precision requires approximately 16GB of VRAM, leaving a comfortable 8GB headroom on the RTX 3090. This ample VRAM allows for larger batch sizes and longer context lengths without encountering memory limitations. The RTX 3090's substantial memory bandwidth (0.94 TB/s) ensures rapid data transfer between the GPU and memory, further enhancing performance during inference. The presence of 10496 CUDA cores and 328 Tensor Cores accelerates the matrix multiplications and other computations crucial for LLM inference, leading to higher throughput and lower latency.
Given the RTX 3090's capabilities, users can experiment with different inference frameworks like `vLLM` or `text-generation-inference` to optimize for throughput or latency. Employing quantization techniques, such as converting the model to INT8 or even lower precision (if supported without significant accuracy loss), can further reduce VRAM usage and potentially increase inference speed. Monitoring GPU utilization and memory consumption is crucial to fine-tune batch sizes and context lengths for optimal performance. Consider using tools like `nvtop` or `nvidia-smi` to track these metrics.