The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is exceptionally well-suited for running the Gemma 2 2B model. Gemma 2 2B in its q3_k_m quantized form requires only 0.8GB of VRAM. This leaves a substantial 23.2GB of VRAM headroom, ensuring smooth operation even with large batch sizes and extended context lengths. The RTX 4090's Ada Lovelace architecture, featuring 16384 CUDA cores and 512 Tensor cores, provides ample computational power for fast inference. The high memory bandwidth minimizes data transfer bottlenecks, further enhancing performance. The q3_k_m quantization reduces the model's memory footprint and computational demands, making it highly efficient on this GPU.
Given the abundant VRAM and computational power, users should explore increasing the batch size to maximize throughput. Experiment with batch sizes up to 32 or even higher, monitoring VRAM usage to avoid exceeding the available capacity. Consider using inference frameworks like `llama.cpp` for CPU+GPU inference or `vLLM` for optimized GPU-only inference to further enhance performance. While the q3_k_m quantization provides a good balance of performance and accuracy, experimenting with slightly higher quantization levels (e.g., q4_k_m) might yield acceptable accuracy with minimal performance impact. Profile the model with different settings to find the optimal balance between speed and quality for your specific application.