The NVIDIA RTX 3090, with its substantial 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Phi-3 Small 7B model, especially when employing quantization techniques. The Q3_K_M quantization reduces the model's VRAM footprint to a mere 2.8GB, leaving a significant 21.2GB of VRAM headroom. This ample VRAM allows for larger batch sizes and longer context lengths without encountering memory limitations. The RTX 3090's impressive memory bandwidth of 0.94 TB/s ensures rapid data transfer between the GPU and memory, which is crucial for maintaining high inference speeds. Furthermore, the 10496 CUDA cores and 328 Tensor Cores within the Ampere architecture provide considerable computational power for accelerating the matrix multiplications and other operations inherent in LLM inference.
Given the RTX 3090's capabilities and the model's size after quantization, users should experiment with increasing the batch size to maximize throughput. Start with the suggested batch size of 15 and incrementally increase it while monitoring GPU utilization and latency. For optimal performance, leverage inference frameworks like `llama.cpp` or `vLLM`, which are optimized for quantized models and GPU acceleration. Also, consider using techniques like speculative decoding to further boost token generation speeds. While Q3_K_M provides excellent VRAM savings, explore slightly higher quantization levels like Q4_K_M if you need even more performance at the cost of minimal accuracy.