The AMD RX 7900 XTX, with its 24GB of GDDR6 VRAM and 0.96 TB/s memory bandwidth, offers ample resources for running the Gemma 2 2B model. Gemma 2 2B, even in its full FP16 precision, only requires about 4GB of VRAM. When quantized to Q4_K_M (4-bit), the VRAM footprint shrinks dramatically to approximately 1GB. This leaves a significant 23GB of VRAM headroom, ensuring the model and its associated data structures can reside comfortably in the GPU's memory without causing performance bottlenecks due to swapping or offloading to system RAM. The RDNA 3 architecture, while lacking dedicated Tensor Cores like NVIDIA GPUs, can still perform matrix multiplications efficiently, contributing to reasonable inference speeds.
Given the substantial VRAM headroom, experiment with larger batch sizes (starting around 32) to maximize throughput. While the Q4_K_M quantization provides a good balance between memory usage and performance, consider experimenting with unquantized FP16 or higher-precision quantization levels if you prioritize accuracy and have the resources. If the estimated 63 tokens/sec isn't sufficient, investigate optimized inference frameworks like llama.cpp with ROCm support, or explore alternative backends that leverage the RX 7900 XTX's compute capabilities more effectively. Ensure that your ROCm drivers are up-to-date for optimal performance.