The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is exceptionally well-suited for running the Phi-3 Medium 14B model, especially when using quantization. The Q4_K_M quantization (4-bit) dramatically reduces the model's VRAM footprint to approximately 7GB. This leaves a substantial 17GB VRAM headroom on the RTX 4090, ensuring that the model and its associated inference processes can operate comfortably without encountering memory limitations. The RTX 4090's Ada Lovelace architecture, featuring 16384 CUDA cores and 512 Tensor cores, provides significant computational power for accelerating inference, further boosting performance.
Given the ample VRAM and high memory bandwidth, the primary performance bottleneck is unlikely to be memory-related. Instead, the limiting factor will likely be the raw compute throughput of the GPU and the efficiency of the inference framework used. Expect a throughput of approximately 60 tokens per second, which is a good rate for interactive applications. The large VRAM headroom also allows for experimentation with larger batch sizes (up to 6 in this case) to potentially increase overall throughput, although this may come at the cost of increased latency for individual requests. The high memory bandwidth ensures that data can be transferred quickly between the GPU and system memory, minimizing stalls during inference.
For optimal performance with Phi-3 Medium 14B on the RTX 4090, leverage the model's full 128,000 token context length. Experiment with different batch sizes to find a balance between throughput and latency; starting with a batch size of 6 is a good baseline. Explore using inference frameworks like `llama.cpp` for CPU+GPU inference or `vLLM` for optimized GPU-only inference. Monitor GPU utilization and VRAM usage during inference to ensure that the system is operating efficiently. If you encounter performance bottlenecks, consider optimizing the prompt structure or reducing the context length slightly.
If you find the 60 tokens/sec insufficient, consider further optimizations like tensor parallelism (if supported by the inference framework and model) or exploring higher levels of quantization (e.g., Q3_K_M), though this may impact the model's accuracy. Remember to profile the application to identify the true bottleneck before making changes.