The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM, falls short of the 28GB required to load the Phi-3 Medium 14B model in FP16 precision. This VRAM deficit means the model, in its full FP16 form, cannot be directly loaded onto the GPU for inference. The RTX 4090's impressive memory bandwidth of 1.01 TB/s would otherwise facilitate rapid data transfer, and its 16384 CUDA cores along with 512 Tensor cores would provide substantial computational power. However, without sufficient VRAM to hold the model, these capabilities cannot be fully utilized. The Ada Lovelace architecture is designed for efficient AI processing, but memory limitations are a critical bottleneck in this scenario.
Even with the RTX 4090's powerful architecture, the insufficient VRAM prevents running the model at its intended precision. While the memory bandwidth and core count are ample for accelerating inference, the inability to load the model entirely into VRAM will result in out-of-memory errors or necessitate alternative strategies like quantization or offloading layers to system RAM, both of which impact performance. Running the model partially on system RAM would significantly reduce the tokens/sec and increase latency, negating many of the RTX 4090's advantages.
To run Phi-3 Medium 14B on the RTX 4090, you'll need to employ quantization techniques. Quantization reduces the memory footprint of the model by representing its weights with fewer bits. Using a framework like `llama.cpp` or `text-generation-inference`, experiment with quantizing the model to 8-bit (INT8) or even 4-bit (GPTQ or AWQ) precision. This will significantly reduce VRAM usage, potentially bringing it within the RTX 4090's 24GB capacity. Be aware that quantization can slightly impact the model's accuracy, so evaluate the trade-off between performance and quality.
If quantization alone isn't enough, consider offloading some layers to system RAM. This is generally slower but can allow you to run the model. Experiment with different layer offloading strategies to find the optimal balance between VRAM usage and performance. Also, reduce the context length and batch size to minimize VRAM consumption. If these optimizations are insufficient, explore cloud-based inference services or consider upgrading to a GPU with more VRAM.