The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is exceptionally well-suited for running the Phi-3 Small 7B model, especially when quantized. The q3_k_m quantization brings the model's VRAM footprint down to a mere 2.8GB, leaving a significant 21.2GB of headroom. This ample VRAM allows for larger batch sizes and longer context lengths without encountering memory limitations. The RTX 3090 Ti's 10752 CUDA cores and 336 Tensor Cores further contribute to efficient computation and acceleration of the model's operations.
Given the substantial memory bandwidth and compute capabilities of the RTX 3090 Ti, users can expect excellent performance with Phi-3 Small 7B. The estimated tokens/second rate of 90 is a strong indicator of responsiveness and usability. A batch size of 15 is also achievable, allowing for parallel processing of multiple requests or longer sequences, further enhancing throughput. The Ampere architecture of the RTX 3090 Ti is optimized for AI workloads, providing hardware-level acceleration for the matrix multiplications and other operations that are fundamental to large language models.
The RTX 3090 Ti is an ideal GPU for running Phi-3 Small 7B. Given the large VRAM headroom, experiment with larger batch sizes to maximize throughput. While q3_k_m quantization is efficient, consider trying slightly higher quantization levels (e.g., q4_k_m) if you need slightly better accuracy, as the 3090 Ti has ample VRAM to spare. Monitor GPU utilization and temperature to ensure optimal performance and stability, especially when running at high batch sizes or with long context lengths.
For optimal performance, use a framework like `llama.cpp` or `vLLM` that leverages the GPU effectively. Ensure you have the latest NVIDIA drivers installed to benefit from the latest optimizations. If you encounter performance bottlenecks, profile your code to identify areas for improvement. Also, consider using techniques like speculative decoding to further improve the tokens/second rate.