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, particularly in its Q4_K_M (4-bit) quantized form. This quantization significantly reduces the model's VRAM footprint to approximately 4GB. Given the RTX 4090's substantial VRAM capacity, a large 20GB headroom exists, allowing for comfortable operation even with larger batch sizes and extended context lengths. The Ada Lovelace architecture's 16384 CUDA cores and 512 Tensor cores further accelerate computations, leading to efficient inference. The high memory bandwidth ensures rapid data transfer between the GPU and memory, preventing bottlenecks during model execution.
The expected performance of 72 tokens per second is a direct result of the GPU's raw compute power and memory capabilities. The Q4_K_M quantization helps to further accelerate the model, by reducing the memory bandwidth requirements. The estimated batch size of 12 can be further optimized depending on the specific inference framework and application requirements. The RTX 4090's architecture and specifications make it an ideal choice for running this model, enabling high throughput and low latency, crucial for real-time applications.
For optimal performance, leverage an inference framework like `llama.cpp` or `vLLM`, which are known for their efficiency with quantized models. Start with a batch size of 12 and experiment with increasing it to maximize GPU utilization, keeping an eye on latency. While the Q4_K_M quantization is a good starting point, consider exploring other quantization levels if you need to further optimize speed or reduce VRAM usage, but be aware of potential accuracy trade-offs. Monitor GPU utilization and memory usage to fine-tune settings and prevent any bottlenecks. Ensure your system has adequate cooling to handle the RTX 4090's 450W TDP.