The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and Ampere architecture, offers substantial resources for running large language models like the Phi-3 Mini 3.8B. This model requires approximately 7.6GB of VRAM when using FP16 precision. The 3090 Ti's memory bandwidth of 1.01 TB/s ensures rapid data transfer between the GPU and memory, which is critical for minimizing latency during inference. Furthermore, the 10752 CUDA cores and 336 Tensor Cores accelerate the matrix multiplications and other computations inherent in transformer-based models like Phi-3.
The ample VRAM headroom (16.4GB) allows for larger batch sizes and longer context lengths, improving throughput and enabling more complex tasks. The Tensor Cores are specifically designed to accelerate mixed-precision computations, further boosting performance. The estimated tokens/second rate of 90 is a solid starting point, but real-world performance will depend on factors like the specific inference framework used, batch size, and prompt complexity. The estimated batch size of 21 is a good starting point for maximizing GPU utilization without exceeding memory capacity.
Given the RTX 3090 Ti's capabilities, focus on optimizing for throughput by increasing the batch size as much as your application's latency requirements allow. Experiment with different inference frameworks like `vLLM` or `text-generation-inference` to leverage their optimized kernels and scheduling algorithms. Quantization to INT8 or even lower precision (if supported by the framework) can further reduce memory footprint and potentially increase inference speed, though this may come at the cost of some accuracy. Monitor GPU utilization and memory usage to fine-tune your settings for optimal performance.
If you encounter performance bottlenecks, consider profiling your code to identify the most computationally intensive parts. Implementing techniques like speculative decoding or using a more efficient attention mechanism could also improve performance. If VRAM becomes a limitation with larger models or longer context lengths in the future, explore offloading some layers to system RAM, though this will significantly reduce inference speed.