The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Phi-3 Small 7B model, particularly when using INT8 quantization. Quantization reduces the model's memory footprint significantly; in this case, down to 7GB. This leaves a substantial 17GB of VRAM headroom. The RTX 3090's impressive memory bandwidth of 0.94 TB/s ensures that data can be transferred quickly between the GPU and memory, preventing bottlenecks during inference. The 10496 CUDA cores and 328 Tensor cores further accelerate the computations required for LLM inference, contributing to fast token generation.
Given the ample VRAM available, you can experiment with larger batch sizes and context lengths to optimize throughput. Start with a batch size of 12, as estimated, and gradually increase it until you observe performance degradation. Explore different inference frameworks like `llama.cpp` or `vLLM` to find the best balance between latency and throughput. Monitor GPU utilization and memory usage to identify potential bottlenecks. If needed, consider using techniques like attention quantization or activation caching to further improve performance, although with 17GB headroom this may not be needed.