The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM, falls short of the 54GB VRAM requirement for running the Gemma 2 27B model in FP16 (half-precision floating point). This memory shortfall means the entire model cannot be loaded onto the GPU simultaneously for inference. The RTX 4090's substantial memory bandwidth (1.01 TB/s) and CUDA core count (16384) would otherwise contribute to fast processing, but the VRAM limitation becomes the primary bottleneck. Without sufficient VRAM, the system would either crash, produce incorrect results, or require offloading parts of the model to system RAM, drastically reducing performance.
Even though the RTX 4090 boasts 512 Tensor Cores, which are designed to accelerate matrix multiplications central to deep learning, they cannot compensate for the lack of VRAM. The Ada Lovelace architecture is efficient, but efficiency cannot overcome the fundamental limitation of fitting the model into available memory. Attempting to run the model without addressing the memory issue will likely result in extremely slow inference speeds or complete failure. The absence of sufficient VRAM headroom means that even small increases in batch size or context length will quickly exhaust available resources.
Due to the significant VRAM deficit, directly running Gemma 2 27B on the RTX 4090 in FP16 is not feasible. To make it work, you must employ aggressive quantization techniques. Quantization reduces the memory footprint of the model by representing weights with fewer bits. Consider using 4-bit quantization (bitsandbytes or GPTQ) or even lower precision formats if supported. Another approach is to offload some layers to the CPU, but this will severely impact performance. Distributed inference across multiple GPUs, if available, would also be an effective solution. Always monitor VRAM usage during inference to ensure you don't exceed the card's capacity.