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 Medium 14B model, especially when utilizing INT8 quantization. The model's 14 billion parameters would typically require 28GB of VRAM in FP16 (full precision). However, INT8 quantization reduces the VRAM footprint to approximately 14GB. This leaves a substantial 10GB VRAM headroom on the RTX 4090, ensuring smooth operation even with larger context lengths or increased batch sizes. The Ada Lovelace architecture and its 512 Tensor Cores further accelerate the matrix multiplications inherent in transformer models, leading to improved inference speeds.
Memory bandwidth is another critical factor. The RTX 4090's 1.01 TB/s bandwidth ensures that data can be moved efficiently between the GPU's memory and processing units. This is crucial for maintaining high throughput during inference, particularly with long context lengths. While the model may fit in VRAM without quantization, the reduced memory bandwidth requirements of INT8 allow for more efficient data transfer, thus improving tokens/sec. The estimated 60 tokens/sec and batch size of 3 are realistic expectations for this configuration, assuming optimized inference settings.
For optimal performance with Phi-3 Medium 14B on the RTX 4090, stick with INT8 quantization. Experiment with different inference frameworks like `llama.cpp`, `vLLM`, or `text-generation-inference` to find the one that offers the best balance of speed and memory efficiency for your specific use case. Start with a batch size of 3 and a context length of 128000 tokens, and then incrementally increase the batch size to maximize GPU utilization, while monitoring for any VRAM issues. Consider using techniques like attention quantization or speculative decoding to further improve inference speed.
If you encounter performance bottlenecks, investigate CPU utilization, as data preprocessing and post-processing can sometimes become limiting factors. Ensure you have sufficient system RAM to handle the model and data efficiently. If you wish to experiment with higher precision (FP16), be mindful of the VRAM limitations and consider reducing the context length or batch size accordingly.