The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Llama 3 8B model, especially when utilizing quantization techniques. The q3_k_m quantization brings the model's VRAM footprint down to a mere 3.2GB. This leaves a substantial 20.8GB of VRAM headroom, allowing for larger batch sizes, longer context lengths, and the potential to run other applications concurrently without encountering memory limitations. The RTX 3090's impressive memory bandwidth of 0.94 TB/s ensures rapid data transfer between the GPU and memory, further enhancing performance.
The RTX 3090's 10496 CUDA cores and 328 Tensor cores also contribute significantly to the model's inference speed. The Tensor cores, specifically designed for accelerating matrix multiplications, are crucial for the efficient execution of deep learning operations. While the estimated tokens/sec of 72 is a good starting point, the actual performance can vary depending on the specific inference framework used and the level of optimization applied. The batch size of 13 is also a reasonable estimate given the VRAM availability, but it can be adjusted based on the desired latency and throughput trade-off.
Given the ample VRAM headroom, experiment with increasing the batch size to maximize throughput. Start with the suggested batch size of 13 and gradually increase it until you observe diminishing returns in tokens/sec or encounter memory errors. Also, explore different inference frameworks like llama.cpp, vLLM, or NVIDIA's TensorRT to find the optimal balance between latency and throughput for your specific use case. For even faster inference, consider further quantization options, but be mindful of the potential impact on model accuracy. Monitor GPU utilization and temperature to ensure the system operates within safe limits, especially when running at high loads.