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Read about our latest product features, solutions, and updates.
GLM 5.2 Batch Processing: High-Volume API Workflows
Processing thousands of requests with GLM 5.2 requires async concurrency, rate limit handling, and cost optimization. Here is how to build efficient batch pipelines that maximize throughput without hitting API limits.
Deploying GLM 5.2 with Docker: Self-Hosting and vLLM API Setup
GLM 5.2's MIT license allows full self-hosting on your own GPU infrastructure. Here is how to containerize a GLM 5.2 API server using Docker and vLLM, with memory requirements and production configuration.
How to Use GLM 5.2 with LangChain: Complete Integration Guide
GLM 5.2 works with LangChain via ChatOpenAI by pointing base_url to Z.ai's API. Here is how to build chains, agents, and tool-calling workflows with GLM 5.2 as the backend model.
GLM 5.2 RAG: Building Retrieval-Augmented Generation Applications
GLM 5.2's 1M-token context window changes the RAG calculus — for many use cases you can skip the retrieval step entirely. Here is how to build both full-context and traditional retrieval-based RAG pipelines with GLM 5.2.
GLM 5.2 Streaming API: Real-Time Token Output Guide
GLM 5.2 supports streaming via the standard OpenAI SSE format. Here is how to implement streaming in Python and JavaScript, handle delta tokens, and build real-time chat interfaces.
GLM 5.2 Function Calling: Tool Use, Parallel Calls, and Agentic Workflows
GLM 5.2 supports OpenAI-compatible function calling with parallel tool calls. Here is how to define tools, handle responses, build multi-step agents, and run real agentic loops.
How to Fine-Tune GLM 5.2: Hardware Requirements and What Is Actually Practical
GLM 5.2 MIT license allows fine-tuning. Full 753B fine-tuning requires 16+ H100s. LoRA on quantized versions is feasible on 2-4 A100s. Here is what works and what does not.
How to Use GLM 5.2 API with Python: Complete Integration Guide
GLM 5.2 is OpenAI SDK-compatible. Change base_url and model name and your existing Python code works. Here is the step-by-step guide with streaming, function calling, and async examples.
How to Use GLM 5.2 for Free: 4 Methods That Work in 2026
GLM 5.2 weights are MIT-licensed but self-hosting needs 240GB+ RAM. The real free options are browser chat, NVIDIA NIM credits, and Cloudflare Workers AI (10k neurons/day). Honest breakdown of all four.