The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, is well-suited for running the Gemma 2 27B model, especially when using quantization. The q3_k_m quantization method significantly reduces the model's VRAM footprint to approximately 10.8GB. This leaves a substantial 13.2GB VRAM headroom, allowing for comfortable operation and potentially accommodating larger batch sizes or longer context lengths without exceeding the GPU's memory capacity. The RTX 3090's high memory bandwidth (0.94 TB/s) ensures efficient data transfer between the GPU and VRAM, which is crucial for maintaining optimal inference speed.
Furthermore, the RTX 3090's 10496 CUDA cores and 328 Tensor cores provide ample computational power for accelerating the matrix multiplications and other operations inherent in large language model inference. While the Ampere architecture is not the newest, it still offers excellent performance for AI workloads. The estimated 60 tokens/sec throughput is a reasonable expectation, but actual performance can vary based on the specific inference framework, batch size, and context length used. The batch size of 2 is a good starting point to balance latency and throughput.
Given the ample VRAM headroom, users can experiment with slightly larger batch sizes to potentially improve overall throughput, but be mindful of latency increases. Using `llama.cpp` for inference is a solid choice for its ease of use and broad compatibility. Ensure you have the latest drivers installed to maximize performance and stability. Monitor GPU utilization and memory usage during inference to identify any potential bottlenecks. If you encounter performance issues, consider further optimizing the model using techniques like knowledge distillation or pruning, although these are more advanced.
Alternatively, for more optimized performance, explore using inference frameworks like vLLM or NVIDIA's TensorRT, but be aware that these may require more setup and configuration. vLLM, in particular, could provide significant speed improvements due to its optimized memory management and continuous batching capabilities. Consider using a profiler to identify specific bottlenecks within your inference pipeline.