
2/11/2026
What this post added
This post details the integration of GLM-5, a frontier open weights foundation language model, with Modal's infrastructure. It discusses the technical challenges of running large models like GLM-5, including its 700GB weight size and the need for multi-GPU deployment. The post outlines the specific parallelism strategies (tensor parallelism in MoE layers with DeepGemm kernels, data parallelism in attention layers with FlashMLA kernels) used for GLM-5 inference. It also mentions ongoing work to integrate inference-optimized paths for Flash Attention 4 and provides reproducible deployment code for running GLM-5 with SGLang on Modal. Additionally, it announces a free endpoint for GLM-5 and provides integration examples for various AI frontend frameworks (OpenCode, OpenClaw, Claude Code via LiteLLM, Vercel AI SDK).