The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, falls short of the 28GB VRAM requirement for running the Phi-3 Medium 14B model in FP16 (half-precision). This means you won't be able to load the entire model into the GPU's memory, preventing successful inference without optimization. While the 3090 Ti boasts a substantial memory bandwidth of 1.01 TB/s and a high number of CUDA and Tensor cores, the primary bottleneck here is the insufficient VRAM capacity.
Even if the model could be squeezed into the available VRAM, the limited headroom would likely result in significantly reduced performance. Operations would be heavily reliant on swapping data between the GPU and system memory, which is considerably slower than on-chip VRAM access. This would lead to lower tokens per second and a smaller effective batch size, making real-time or interactive applications impractical. Memory bandwidth, while high, cannot compensate for the fundamental lack of sufficient VRAM to hold the entire model.
To run Phi-3 Medium 14B on your RTX 3090 Ti, you'll need to employ quantization techniques. Quantization reduces the memory footprint of the model by representing its weights and activations with fewer bits. Consider using a quantization level like Q4_K_M or even lower (e.g., Q2_K) if necessary. This will significantly reduce the VRAM requirements, potentially bringing it within the 3090 Ti's 24GB capacity.
Alternatively, explore offloading some layers of the model to system RAM. Frameworks like llama.cpp allow for this, but it will noticeably decrease inference speed. Before attempting this, thoroughly explore quantization options. If performance is critical and quantization is insufficient, consider using a more powerful GPU with more VRAM, such as an RTX 4090 or a professional-grade GPU like an A100.