The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and Ada Lovelace architecture, is exceptionally well-suited for running the Phi-3 Small 7B model. Phi-3 Small 7B, a 7 billion parameter language model, requires significantly less VRAM than the 4090 provides, especially when quantized to INT8. Quantization reduces the model's memory footprint from FP16's 14GB to a mere 7GB, leaving a substantial 17GB of VRAM headroom. This ample VRAM allows for larger batch sizes and longer context lengths, enhancing throughput and overall performance.
Furthermore, the RTX 4090's high memory bandwidth of 1.01 TB/s ensures rapid data transfer between the GPU and memory, preventing bottlenecks during inference. The 16384 CUDA cores and 512 Tensor cores further accelerate computations, enabling efficient matrix multiplications crucial for LLM processing. The estimated 90 tokens/sec reflects the synergy between the model's size, the GPU's capabilities, and the INT8 quantization. The large VRAM headroom also allows for experimentation with larger batch sizes, potentially further increasing the tokens/sec.
Given the substantial VRAM headroom, users should experiment with increasing the batch size to maximize GPU utilization and throughput. Start with the suggested batch size of 12 and incrementally increase it until you observe diminishing returns or encounter VRAM limitations. Utilizing inference frameworks optimized for NVIDIA GPUs, such as TensorRT or vLLM, can further boost performance through kernel fusion and other optimizations. Consider using techniques like speculative decoding to further improve performance.
While INT8 quantization provides a good balance between performance and memory usage, consider experimenting with lower precision quantization levels (e.g., INT4) if you are willing to trade off some accuracy for even greater speed and memory efficiency. However, always carefully evaluate the impact of quantization on the model's output quality. Additionally, ensure that your system has adequate cooling to handle the RTX 4090's 450W TDP, especially when running demanding workloads for extended periods.