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 8B model, especially in its Q4_K_M (4-bit) quantized form. The quantized model requires only 4GB of VRAM, leaving a substantial 20GB headroom. This ample VRAM allows for larger batch sizes and longer context lengths without encountering memory limitations. The RTX 4090's Ada Lovelace architecture, featuring 16384 CUDA cores and 512 Tensor cores, provides significant computational power for accelerating inference. The high memory bandwidth ensures rapid data transfer between the GPU and memory, further enhancing performance.
Given the RTX 4090's capabilities and the model's relatively small footprint in its quantized form, users should aim to maximize batch size and context length to optimize throughput. Experiment with different batch sizes, starting around 12, to find the optimal balance between latency and throughput for your specific application. Consider using a higher precision (e.g., FP16) if the increased VRAM usage is acceptable, as this can improve the model's accuracy. If you encounter performance bottlenecks, profile your code to identify areas for optimization, such as kernel launch overhead or data transfer inefficiencies.