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 quantized version (q3_k_m) of Phi-3 Mini requires only 1.5GB of VRAM, leaving a substantial 22.5GB of 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 model's computations, leading to faster inference times. The Ada Lovelace architecture's advancements in tensor core utilization contribute to efficient matrix multiplications, which are fundamental to deep learning operations. The high memory bandwidth ensures that data can be transferred quickly between the GPU and memory, preventing bottlenecks during inference.
For optimal performance, leverage the RTX 4090's capabilities by experimenting with larger batch sizes to maximize throughput. Start with the estimated batch size of 29 and adjust based on observed performance and latency requirements. Consider using the full context length of 128000 tokens to take advantage of the model's long-range dependencies. While the q3_k_m quantization provides a good balance between memory usage and performance, explore other quantization levels to fine-tune the trade-off according to your specific needs. If you encounter any performance bottlenecks, profile your application to identify areas for optimization, such as kernel fusion or memory access patterns.