The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, provides sufficient memory to comfortably run the Q4_K_M quantized version of the Gemma 2 27B model, which requires approximately 13.5GB of VRAM. The RTX 3090's memory bandwidth of 0.94 TB/s will allow for efficient data transfer between the GPU and VRAM, which is crucial for inference speed. The Ampere architecture, featuring 10496 CUDA cores and 328 Tensor cores, accelerates the matrix multiplications and other computations inherent in large language models, further enhancing performance. The 10.5GB of VRAM headroom also leaves space for larger batch sizes or longer context lengths, although these will likely be limited by performance considerations.
For optimal performance, leverage llama.cpp or similar inference frameworks that are optimized for quantized models. Begin with a batch size of 1 and a context length of 8192 tokens, then experiment with increasing the batch size to maximize GPU utilization, while monitoring for any performance degradation. Consider using techniques like attention quantization or speculative decoding to further boost tokens/sec if needed. Keep the GPU temperature in check given the 350W TDP.