Idea for how to run GLM2 at a decent quant, need critique/feedback

The article discusses a proposed setup for running the GLM2 model efficiently using a rig with four NVIDIA 5060 Ti GPUs, leveraging 64 GB of VRAM and aiming to optimize for inference tasks. The author suggests enhancing the system with 512 GB of DDR3 RAM on a compatible server motherboard, such as the Supermicro X9DRi-F, to achieve low-latency performance with the Qwen/Qwen3.6-27B-FP8 model, targeting 72 tokens per second at a maximum context of 262k. This configuration aims to address compute bottlenecks while minimizing costs, making it a potentially viable solution for practitioners focusing on high-performance inference with large language models.