The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is exceptionally well-suited for running the Phi-3 Mini 3.8B model. The model, when quantized to INT8, requires only 3.8GB of VRAM, leaving a substantial 20.2GB headroom. This ample VRAM allows for large batch sizes and extended context lengths without encountering memory limitations. The RTX 4090's 16384 CUDA cores and 512 Tensor cores further accelerate the matrix multiplications and other computations inherent in transformer-based language models like Phi-3 Mini. The high memory bandwidth ensures rapid data transfer between the GPU and VRAM, minimizing bottlenecks and maximizing throughput.
The estimated 90 tokens/sec performance reflects the RTX 4090's ability to process the model efficiently. This estimate is based on typical performance benchmarks for similar models and hardware configurations. The large VRAM headroom also enables experimentation with larger batch sizes, potentially further increasing throughput. However, the actual performance can vary depending on the specific inference framework used, the input prompt complexity, and other system configurations. Using INT8 quantization significantly reduces the memory footprint and computational requirements, making the model more accessible and faster to run on consumer-grade hardware.
Given the RTX 4090's capabilities, users should explore maximizing batch size to improve throughput. Start with a batch size of 26, as suggested, and experiment with increasing it until VRAM utilization approaches its limit. Also, prioritize using an optimized inference framework like `vLLM` or `text-generation-inference` to leverage features such as continuous batching and optimized kernel implementations. Consider experimenting with mixed precision (e.g., FP16 or BF16) if you need to trade off some accuracy for speed, although INT8 is likely sufficient for many use cases.
For optimal performance, ensure your system has sufficient CPU cores and RAM to handle data preprocessing and post-processing. Monitor GPU utilization and VRAM usage during inference to identify any potential bottlenecks. If performance is still not satisfactory, consider offloading some tasks to the CPU or exploring further quantization techniques, such as INT4.