The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM, offers ample memory to comfortably run the Phi-3 Small 7B model, which requires approximately 14GB of VRAM when using FP16 precision. This leaves a substantial 10GB VRAM headroom, ensuring smooth operation even with larger batch sizes or longer context lengths. The RTX 4090's high memory bandwidth of 1.01 TB/s further contributes to efficient data transfer between the GPU and memory, minimizing potential bottlenecks during inference. The Ada Lovelace architecture, with its 16384 CUDA cores and 512 Tensor cores, is well-suited for accelerating the matrix multiplications and other computations that form the core of LLM inference.
Given the generous VRAM headroom, you can experiment with larger batch sizes (up to 7) to maximize throughput, especially when serving multiple concurrent requests. Consider using a context length of up to 128000 tokens, as supported by the model, to fully leverage its capabilities for tasks requiring long-range dependencies. For optimal performance, explore quantization techniques like INT8 or even INT4, which could further reduce memory footprint and increase inference speed without significant loss in accuracy. Monitor GPU utilization and memory usage to fine-tune batch size and other parameters for your specific workload.