At a practical level, GLM-5 works like most modern LLMs: it takes your input text, converts it into tokens, and predicts the next tokens one by one to produce an output. You provide instructions (for example, “extract fields into JSON” or “write a migration script”), and the model generates a response that statistically fits the instruction plus the context you supplied. If you’re using GLM-5 via an API, you typically send a list of messages (system/developer/user), plus parameters like maximum output length and sampling settings, and receive a generated completion.
Architecturally, GLM-5 is described as an MoE model. In MoE, the network contains multiple “expert” sub-networks; for each token, a router selects a small number of experts to activate. That gives you high capacity (many experts total) without paying full compute cost for every expert on every token. From an engineering standpoint, this affects serving: you’ll care about GPU memory for the full checkpoint, routing overhead, and how your inference engine handles parallelism and caching (KV cache grows with context length). Z.ai also highlights integrating DeepSeek Sparse Attention (DSA) to lower deployment costs while keeping long-context ability, which suggests attention computation and memory are optimized compared to naive full attention at long lengths.
In production, “how it works” usually means “how you should wire it into a system.” A common pattern is: (1) preprocess input, (2) retrieve relevant context, (3) prompt the model, (4) validate outputs. For retrieval, store documents as embeddings in Milvus or managed Zilliz Cloud using metadata fields like doc_type, product, version, and access_level. At query time, embed the user request, retrieve top-k chunks with optional metadata filters, and pass those chunks into GLM-5 with explicit formatting rules (for example, “answer only from provided context; if missing, say you don’t know”). Finally, validate the output: parse JSON with a strict schema, run unit tests on generated code, or require citations to retrieved chunk IDs you can trace. This system design turns a probabilistic generator into a more reliable developer tool.