The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM and Ampere architecture, is exceptionally well-suited for running the Phi-3 Small 7B model. Phi-3 Small 7B in FP16 precision requires approximately 14GB of VRAM, leaving a substantial 10GB headroom on the RTX 3090. This ample VRAM allows for comfortable operation without encountering memory limitations, even when dealing with extended context lengths or larger batch sizes. The RTX 3090's memory bandwidth of 0.94 TB/s further contributes to efficient data transfer between the GPU and memory, minimizing potential bottlenecks during inference.
The RTX 3090's 10496 CUDA cores and 328 Tensor Cores provide significant computational power for accelerating the matrix multiplications and other operations inherent in LLM inference. The Ampere architecture's improvements in Tensor Core utilization further enhance performance. Given these specifications, the RTX 3090 can process Phi-3 Small 7B at a reasonable speed, with estimated performance reaching around 90 tokens per second. This allows for interactive and responsive conversational AI experiences.
Given the RTX 3090's capabilities, start with FP16 precision for Phi-3 Small 7B to maximize speed and efficiency. Experiment with batch sizes around 7 to optimize throughput. If you encounter VRAM limitations when increasing context length or batch size, consider using quantization techniques like Q4_K_M or Q5_K_M to reduce the model's memory footprint. Monitoring GPU utilization and memory usage during inference is crucial for fine-tuning settings and identifying potential bottlenecks.
For optimal performance, leverage inference frameworks like `vLLM` or `text-generation-inference`. These frameworks offer optimized kernels and memory management strategies specifically designed for LLMs, leading to improved throughput and reduced latency compared to naive implementations. If you are using `llama.cpp`, ensure you are using the latest version and have properly configured the BLAS backend for GPU acceleration.