The NVIDIA RTX 3090, with its substantial 24GB of GDDR6X VRAM and 0.94 TB/s memory bandwidth, is exceptionally well-suited for running the Phi-3 Small 7B model. The Q4_K_M quantization significantly reduces the model's memory footprint to approximately 3.5GB, leaving a large VRAM headroom of 20.5GB. This ample VRAM allows for comfortable operation, preventing potential out-of-memory errors and enabling larger batch sizes for increased throughput. The RTX 3090's 10496 CUDA cores and 328 Tensor Cores will accelerate the matrix multiplications and other computations inherent in the model, leading to faster inference speeds.
While VRAM is the primary concern for model loading, the RTX 3090's high memory bandwidth ensures that data can be transferred quickly between the GPU and system memory. This is particularly important for large context lengths, as the model needs to access and process a significant amount of data. The Ampere architecture's improvements in memory management and computational efficiency further contribute to the overall performance of the Phi-3 Small 7B model on this GPU. The estimated tokens/sec of 90 and batch size of 14 are reasonable expectations given the GPU's capabilities and the model's size.
Given the RTX 3090's capabilities, you can experiment with different inference frameworks to optimize performance. Start with llama.cpp for ease of use and broad compatibility, or explore vLLM for potentially higher throughput. Since you have substantial VRAM headroom, consider increasing the batch size to further improve tokens/sec. Be mindful of the context length; while Phi-3 supports up to 128000 tokens, longer context lengths will consume more VRAM and may impact inference speed. Monitor GPU utilization and memory usage to fine-tune settings for optimal performance.
If you encounter performance bottlenecks, consider profiling the model execution to identify the most computationally intensive operations. Optimize these operations by leveraging CUDA kernels or other hardware-specific optimizations. If you're not already using it, enabling memory mapping can help manage large models that might otherwise exceed available RAM.