The NVIDIA RTX 3090 Ti, 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 utilizing quantization. The specified q3_k_m quantization brings the model's VRAM footprint down to a manageable 10.8GB, leaving a substantial 13.2GB of headroom. This generous VRAM allocation ensures that the model and its intermediate calculations can comfortably reside on the GPU, avoiding performance bottlenecks associated with swapping data between the GPU and system RAM. The 3090 Ti's 10752 CUDA cores and 336 Tensor Cores further contribute to efficient computation and acceleration of the model's matrix multiplications, which are fundamental to deep learning inference.
Given the ample VRAM headroom, users can experiment with slightly larger batch sizes to improve throughput, although a batch size of 2 is a good starting point. It's crucial to use an optimized inference framework like `llama.cpp` or `vLLM` to fully leverage the RTX 3090 Ti's capabilities. While q3_k_m quantization is effective, consider testing slightly higher quantization levels (e.g., q4_k_m) if you observe any noticeable quality degradation; the 3090 Ti has sufficient VRAM to accommodate it. Monitor GPU utilization and temperature to ensure the card is operating within safe thermal limits, especially given its 450W TDP.