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 in its Q4_K_M (4-bit) quantized form. The quantized model requires only 3.5GB of VRAM, leaving a substantial 20.5GB headroom. This ample VRAM allows for larger batch sizes and extended context lengths without encountering memory limitations. The RTX 3090 Ti's 10752 CUDA cores and 336 Tensor Cores provide significant computational power, crucial for accelerating the matrix multiplications and other operations inherent in transformer-based language models like Phi-3. The high memory bandwidth ensures that data can be transferred quickly between the GPU and memory, minimizing bottlenecks during inference.
Given the RTX 3090 Ti's robust specifications and the model's small memory footprint in its quantized form, users should prioritize maximizing batch size and context length to optimize throughput. Experiment with different inference frameworks such as `llama.cpp` or `text-generation-inference` to find the best performance. While the Q4_K_M quantization is efficient, exploring FP16 or even higher precision formats might be feasible given the available VRAM, potentially improving output quality at the cost of slightly reduced speed. Monitor GPU utilization and memory usage to fine-tune settings for optimal performance.