The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is exceptionally well-suited for running the Llama 3.1 8B model, especially when quantized to INT8. The INT8 quantization reduces the model's VRAM footprint to approximately 8GB, leaving a significant 16GB headroom on the RTX 4090. This ample VRAM allows for larger batch sizes and longer context lengths without encountering memory limitations. The Ada Lovelace architecture of the RTX 4090, combined with its 16384 CUDA cores and 512 Tensor cores, provides substantial computational power for efficient inference. The high memory bandwidth ensures rapid data transfer between the GPU and memory, further enhancing performance.
Given the substantial VRAM headroom, users can experiment with larger batch sizes to improve throughput. Utilizing inference frameworks optimized for NVIDIA GPUs, such as TensorRT or vLLM, can further enhance performance. While INT8 quantization offers a good balance between performance and accuracy, consider experimenting with FP16 or BF16 if higher precision is required and the performance impact is acceptable. Regularly monitor GPU utilization and memory consumption to identify potential bottlenecks and optimize accordingly. Ensure that you have the latest NVIDIA drivers installed to maximize compatibility and performance.