The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Phi-3 Small 7B model, particularly in its INT8 quantized form. The INT8 quantization significantly reduces the model's VRAM footprint to approximately 7GB, leaving a substantial 17GB of headroom. This ample VRAM capacity ensures that the entire model, along with intermediate calculations and batch processing, can reside comfortably within the GPU's memory, preventing performance-hampering data transfers between the GPU and system RAM.
Furthermore, the RTX 3090 Ti's impressive memory bandwidth of 1.01 TB/s ensures rapid data access, crucial for the iterative computations involved in large language model inference. The 10752 CUDA cores and 336 Tensor Cores provide significant parallel processing power, enabling efficient matrix multiplications and other computationally intensive operations. This combination of high memory bandwidth and abundant compute resources directly translates to faster inference speeds and higher throughput.
Based on these specifications, users can expect robust performance from Phi-3 Small 7B on the RTX 3090 Ti. While specific tokens/second will vary based on prompt complexity and other factors, an estimated rate of 90 tokens/sec and a batch size of 12 is a reasonable expectation with INT8 quantization.
For optimal performance, leverage inference frameworks like `llama.cpp` or `vLLM`, which are optimized for efficient execution of large language models on NVIDIA GPUs. Experiment with different batch sizes to find the sweet spot between throughput and latency. While INT8 quantization provides a good balance of VRAM usage and performance, consider exploring lower precision quantization methods (e.g., INT4) if you need to further reduce VRAM consumption or if your application is less sensitive to minor accuracy differences.
Monitor GPU utilization during inference to identify potential bottlenecks. If the GPU is not fully utilized, increase the batch size or context length. If you encounter memory errors, reduce the batch size or switch to a more aggressive quantization method. Also, make sure you are using the latest NVIDIA drivers for the best performance.