The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Llama 3 8B model, especially when using INT8 quantization. Quantization reduces the model's memory footprint, bringing the VRAM requirement down to approximately 8GB. This leaves a substantial 16GB of VRAM headroom, allowing for larger batch sizes, longer context lengths, and potentially the ability to run other applications concurrently without encountering memory limitations. The RTX 3090 Ti's impressive memory bandwidth of 1.01 TB/s further ensures that data can be transferred quickly between the GPU and memory, minimizing bottlenecks during inference. The 10752 CUDA cores and 336 Tensor Cores will also contribute to accelerating the matrix multiplications and other computations inherent in running large language models.
For optimal performance, start with a batch size of 10 and a context length of 8192 tokens, as initially estimated. Experiment with increasing the batch size to maximize GPU utilization, keeping a close eye on VRAM usage to avoid exceeding the available 24GB. Consider using `llama.cpp` or `vLLM` as your inference framework, as they are known for their efficiency and optimization for NVIDIA GPUs. If you encounter performance bottlenecks, explore techniques such as attention quantization or kernel fusion to further improve throughput.