The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is well-suited for running the Gemma 2 27B model, especially when using quantization. The Q4_K_M (4-bit) quantization reduces the model's VRAM footprint to approximately 13.5GB, leaving a substantial 10.5GB VRAM headroom on the RTX 4090. This headroom ensures smooth operation and allows for larger context lengths or potentially running other smaller tasks concurrently. The RTX 4090's Ada Lovelace architecture, featuring 16384 CUDA cores and 512 Tensor cores, further accelerates the model's inference, enabling faster processing times.
For optimal performance, leverage the RTX 4090's Tensor Cores by using an inference framework that supports CUDA acceleration, such as `llama.cpp` with CUDA enabled, `vLLM`, or `text-generation-inference`. While the Q4_K_M quantization provides a good balance between VRAM usage and accuracy, experiment with slightly higher quantization levels (e.g., Q5_K_M or Q6_K_M) if you have sufficient VRAM and prioritize higher quality output. Monitor VRAM usage to avoid swapping to system memory, which can significantly degrade performance.