The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, is well-suited to run the Llama 3 8B model, especially when quantized to INT8. Quantization reduces the model's memory footprint, bringing it down to approximately 8GB. This leaves a significant 16GB VRAM headroom, allowing for larger batch sizes and longer context lengths without exceeding the GPU's memory capacity. The RTX 3090's high memory bandwidth of 0.94 TB/s ensures rapid data transfer between the GPU and memory, preventing memory bandwidth from becoming a bottleneck during inference. The Ampere architecture, with its 10496 CUDA cores and 328 Tensor cores, provides substantial computational power for accelerating matrix multiplications and other operations crucial for LLM inference.
For optimal performance, utilize an inference framework like `llama.cpp` or `vLLM`, which are optimized for running LLMs on NVIDIA GPUs. Given the ample VRAM headroom, experiment with larger batch sizes to increase throughput. Start with a batch size of 10 and adjust based on observed performance. Monitor GPU utilization to ensure it remains high, indicating efficient use of the available resources. Consider using techniques like speculative decoding to further enhance token generation speed.