The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, offers substantial resources for running large language models. Gemma 2 27B, a 27 billion parameter model, presents a significant memory footprint. However, through quantization, specifically the q3_k_m method, the VRAM requirement is reduced to a manageable 10.8GB. This is well within the RTX 4090's capacity, leaving a comfortable 13.2GB of VRAM headroom. This headroom allows for larger batch sizes or the potential to run other applications concurrently without encountering memory constraints.
Given the RTX 4090's Ada Lovelace architecture, equipped with 16384 CUDA cores and 512 Tensor cores, the model benefits from hardware acceleration during inference. The high memory bandwidth ensures rapid data transfer between the GPU and VRAM, minimizing bottlenecks. While the TDP of 450W indicates significant power consumption, it also reflects the card's ability to sustain high computational throughput. The estimated 60 tokens/sec provides a reasonable inference speed, influenced by factors like prompt complexity and specific implementation details. A batch size of 2 is a starting point that may be adjusted to optimize throughput versus latency, depending on the application.
For optimal performance, utilize an inference framework like `llama.cpp` or `vLLM`, which are known for their efficient memory management and hardware acceleration capabilities. Begin with the provided q3_k_m quantization and a batch size of 2, then experiment to find the best balance between throughput and latency for your specific use case. Monitor GPU utilization and VRAM consumption to ensure stability and prevent out-of-memory errors. Consider adjusting the context length if memory becomes a constraint, although 8192 tokens should be manageable with the given setup.
If you encounter performance bottlenecks, explore further quantization options (e.g., smaller bit widths) or model pruning techniques to reduce the model's size and computational demands. Additionally, ensure that your NVIDIA drivers are up-to-date to leverage the latest performance optimizations. If you are running on a system with limited CPU resources, offloading more processing to the GPU can also improve performance.