Show HN: Self-hosting unpruned GLM-5.2 on a 4-node DGX Spark cluster (github.com)

A new deployment strategy for the unpruned GLM-5.2 model has been successfully demonstrated on a four-node DGX Spark cluster, utilizing a configuration that can process up to 327K context length with impressive throughput rates of around 25 tokens per second. The setup employs several advanced techniques, including TP4, DCP, and MTP speculative decoding with FP8 sparse-MLA key-value caching. This system is designed to leverage the latest DGX Spark firmware and optimize memory utilization across different lanes, allowing for single and multi-user settings by seamlessly switching configurations with a single environment variable. This development is significant for the AI/ML community as it illustrates the potential of scaling large, intricate models like GLM-5.2 using state-of-the-art hardware and tuning practices, ensuring higher throughput and efficient processing. Key technical details emphasize the importance of firmware compatibility, where specific versions must be adhered to in order to prevent compilation errors. Additionally, the initiative showcases the ability to serve diverse workloads effectively by adjusting parameters for context depth and user throughput, highlighting advancements in model serving that can facilitate broader applications of large language models in real-world scenarios.