The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, boasts ample memory to comfortably host the quantized Phi-3 Medium 14B model. Specifically, the Q4_K_M (GGUF 4-bit) quantization brings the model's VRAM footprint down to a manageable 7GB. This leaves a substantial 17GB VRAM headroom, allowing for larger batch sizes, longer context lengths, and potentially the simultaneous operation of other tasks or models. The RTX 3090's 0.94 TB/s memory bandwidth ensures rapid data transfer between the GPU and memory, crucial for minimizing latency during inference. Furthermore, the 10496 CUDA cores and 328 Tensor cores provide significant computational power, enabling efficient execution of the model's matrix multiplications and other operations.
For optimal performance with the Phi-3 Medium 14B model on the RTX 3090, leveraging a framework like `llama.cpp` or `text-generation-inference` is highly recommended. These frameworks are optimized for running large language models and can take full advantage of the RTX 3090's hardware capabilities. Experiment with batch sizes around 6 and a context length of 128000 tokens, as suggested by the initial analysis. Monitor GPU utilization and memory consumption to fine-tune these parameters for your specific use case. If you encounter performance bottlenecks, consider further quantization to reduce VRAM usage or offloading some layers to the CPU, though this may impact inference speed.