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 Mini 3.8B model, especially in its quantized form. The q3_k_m quantization significantly reduces the model's VRAM footprint to approximately 1.5GB. This leaves a substantial 22.5GB VRAM headroom, ensuring that the model and its associated runtime environment have ample resources to operate without memory constraints. The 3090 Ti's Ampere architecture, featuring 10752 CUDA cores and 336 Tensor cores, provides significant computational power for accelerating the matrix multiplications and other operations inherent in large language model inference.
Given the high memory bandwidth, the RTX 3090 Ti can efficiently transfer model weights and intermediate activations between memory and the GPU's compute units. This is crucial for maintaining high throughput and low latency during inference. The combination of abundant VRAM, high memory bandwidth, and powerful compute capabilities makes the RTX 3090 Ti an ideal platform for running Phi-3 Mini 3.8B, even with longer context lengths and larger batch sizes.
Given the substantial VRAM headroom, explore increasing the batch size to maximize GPU utilization and throughput. Experiment with different inference frameworks like `llama.cpp`, `vLLM`, or `text-generation-inference` to determine which provides the best performance for your specific use case. While q3_k_m is efficient, you might also consider experimenting with slightly higher quantization levels (e.g., q4_k_m) to potentially improve output quality without significantly impacting performance. Monitor GPU utilization and memory usage to fine-tune settings for optimal performance.