The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Phi-3 Mini 3.8B model, especially when quantized to q3_k_m. This quantization reduces the model's VRAM footprint to a mere 1.5GB, leaving a substantial 22.5GB of headroom. This ample VRAM allows for larger batch sizes and longer 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, further enhancing performance.
Furthermore, the RTX 3090's 10496 CUDA cores and 328 Tensor cores provide significant computational power for accelerating the matrix multiplications and other operations inherent in LLM inference. The Ampere architecture is optimized for AI workloads, enabling efficient execution of the Phi-3 Mini model. This combination of ample VRAM, high memory bandwidth, and powerful compute capabilities ensures smooth and responsive inference.
Given the RTX 3090's capabilities and the Phi-3 Mini model's relatively small size (especially after quantization), you should experiment with maximizing batch size to improve throughput. Start with a batch size of 29 and gradually increase it until you observe diminishing returns in terms of tokens/sec or encounter memory errors. Also, leverage the full 128000 token context length to maintain coherence in longer conversations or analyses. Using an optimized inference framework like `llama.cpp` or `vLLM` will further enhance performance by efficiently utilizing the GPU's resources.
Consider using techniques like speculative decoding if supported by the inference framework. This can potentially increase the token generation speed by predicting the next few tokens and verifying them in parallel. Monitoring GPU utilization is crucial to identify any bottlenecks. If the GPU is not fully utilized, try increasing the batch size or exploring other optimization techniques.