The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is well-suited for running the Llama 3.1 8B model, especially when using INT8 quantization. The model requires approximately 8GB of VRAM in INT8, leaving a substantial 16GB headroom on the 3090 Ti. This ample VRAM allows for larger batch sizes and longer context lengths without encountering memory limitations. The 3090 Ti's 10752 CUDA cores and 336 Tensor cores also contribute significantly to the model's inference speed, accelerating matrix multiplications and other computationally intensive operations. The Ampere architecture further enhances performance through features like sparsity and mixed-precision computing.
For optimal performance, utilize an inference framework like `llama.cpp` or `vLLM`, which are designed to efficiently handle quantized models. Experiment with batch sizes around 10, as the available VRAM allows for this. While the default context length is 128000 tokens, consider reducing it if you encounter performance bottlenecks or if your specific use case doesn't require such a large context window. Ensure that your NVIDIA drivers are up to date to take advantage of the latest performance optimizations.