The primary limiting factor for running large language models (LLMs) like Gemma 2 27B is VRAM. This model, even when quantized to INT8, requires approximately 27GB of VRAM to load and operate. The AMD RX 7900 XTX, while a powerful GPU, only offers 24GB of VRAM. This 3GB deficit means the model, in its current configuration, cannot be fully loaded onto the GPU, leading to a 'FAIL' compatibility verdict. Memory bandwidth, although substantial at 0.96 TB/s, becomes less relevant when the model cannot reside entirely within the GPU's memory.
Furthermore, the absence of dedicated Tensor Cores on the RX 7900 XTX means that the GPU will rely on its general-purpose compute units for inference. While these units are capable, they are not optimized for the matrix multiplications that are central to LLM processing. This can lead to slower inference speeds compared to GPUs with Tensor Cores. The RDNA 3 architecture offers some optimizations for AI workloads, but these are unlikely to fully compensate for the lack of dedicated hardware acceleration in this specific scenario.
Without sufficient VRAM, the model will either fail to load, or the system will attempt to use system RAM as overflow, which drastically reduces performance due to the slower transfer speeds between system RAM and the GPU. This would result in extremely slow token generation, rendering the model practically unusable for real-time applications.
Given the VRAM limitation, running Gemma 2 27B (INT8) on the RX 7900 XTX is not directly feasible without modifications. The most practical approach would be to explore further quantization techniques, such as Q4 or even Q2, to reduce the VRAM footprint of the model. Additionally, consider using inference frameworks that are optimized for AMD GPUs, such as those leveraging ROCm, to maximize the available performance. Unfortunately, even with optimization, performance will likely be significantly lower than on comparable NVIDIA GPUs with ample VRAM and Tensor Cores. If possible, consider using a cloud-based solution or a GPU with more VRAM for optimal performance.
Another approach is to explore techniques like model parallelism, where the model is split across multiple GPUs. However, this requires significant engineering effort and specialized software support. For most users, exploring aggressive quantization is the most accessible path. Also, be aware that even if the model loads, the context length might need to be reduced significantly to fit within the available VRAM, impacting the model's ability to handle longer inputs.