The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Phi-3 Medium 14B model, especially when employing quantization. The q3_k_m quantization reduces the model's VRAM footprint to approximately 5.6GB, leaving a substantial 18.4GB of VRAM headroom. This surplus allows for comfortable operation, accommodating larger batch sizes and extended context lengths without encountering memory constraints. The RTX 3090's high memory bandwidth (0.94 TB/s) ensures rapid data transfer between the GPU and memory, preventing bottlenecks during inference. Furthermore, the abundance of CUDA cores (10496) and Tensor Cores (328) accelerate the matrix multiplications and other computationally intensive operations inherent in LLM inference.
Given the ample VRAM and computational power of the RTX 3090, users should prioritize maximizing throughput and response quality. Experiment with larger batch sizes (up to 6) to improve tokens/sec. While the provided context length of 128000 tokens is supported, consider the specific use case. For tasks not requiring such extensive context, reducing the context length could further improve inference speed. Additionally, explore different inference frameworks to optimize performance; llama.cpp is a solid starting point for its flexibility and broad compatibility, but vLLM or TensorRT might offer further speed improvements.