Zhipu officially released its next-generation open-source flagship model GLM-5.2, the latest flagship targeting Long Horizon Tasks. Compared to its predecessor GLM-5.1, it achieves a significant leap in long-horizon task capabilities and is open-sourced under the MIT License. The FlagOS Zhongzhi Community completed multi-chip adaptation and inference deployment at the first opportunity, currently covering four chips:
Moore Threads S5000, T-Head 810E, Metax C550 and Hygon DCU BW1000.
Developers can rapidly deploy via the FlagOS unified, open-source software stack; model files and deployment guides are simultaneously available on ModelScope and HuggingFace. GLM-5.2 is a model featuring a stable and usable 1M context window, purpose-built for Long Horizon Tasks. Its core capabilities include:
Solid 1M context: Stably supports a 1,000,000-token context window for long-horizon workloads
Flexible advanced coding: Enhanced coding capabilities with support for multiple inference effort levels to balance performance and latency
Improved architecture: Introduces IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at 1M context length; improves the MTP layer to support speculative decoding, increasing acceptance length by up to 20%
Fully open-source: MIT license, with no geographic restrictions
Integrated Deployment
Out-of-the-box inference scripts with pre-configured hardware and software parameters
Released FlagOS-Metax container image supporting deployment within minutes
Consistency Validation
Rigorously evaluated through benchmark testing: Performance and results from the FlagOS software stack are compared against native stacks on multiple public.
Evaluation Results
Benchmark Result
Metrics
GLM-5.2-Nvidia-Origin
GLM-5.2-Metax-FlagOS
GPQA_Diamond
85.85
84.34
musr_generative
69.2
Evaluating
User Guide
Environment Setup
Item
Version
Docker Version
Docker version 27.5.1, build 27.5.1-0ubuntu3~22.04.2
This inference deployment requires 4 physical machines. All node startup scripts are located under /data/GLM-5.2/script/, with the filename prefix start_.
Full list of scripts:
/data/GLM-5.2/script/start_node0_tp32_pytorch.sh
/data/GLM-5.2/script/start_node1_tp32_pytorch.sh
/data/GLM-5.2/script/start_node2_tp32_pytorch.sh
/data/GLM-5.2/script/start_node3_tp32_pytorch.sh
You need to modify the service startup scripts for the four machines according to your actual environment.
bash
1cd /data/GLM-5.2/script
23# Run on node04bash start_node0_tp32_pytorch.sh
56# Run on node17bash start_node1_tp32_pytorch.sh
89# Run on node210bash start_node2_tp32_pytorch.sh
1112# Run on node313bash start_node3_tp32_pytorch.sh
Choose the appropriate version for your OS (Windows/macOS/Linux)
Follow the installation wizard to complete the setup
2. Configuration
Launch AnythingLLM
Open settings (bottom left, fourth tab)
Configure core LLM parameters
Click "Save Settings" to apply changes
3. Model Interaction
After model loading is complete:
Click "New Conversation"
Enter your question (e.g., “Explain the basics of quantum computing”)
Click the send button to get a response
Technical Overview
FlagOS is a fully open-source system software stack designed to unify the "model–system–chip" layers and foster an open, collaborative ecosystem. It enables a “develop once, run anywhere” workflow across diverse AI accelerators, unlocking hardware performance, eliminating fragmentation among vendor-specific software stacks, and substantially lowering the cost of porting and maintaining AI workloads. With core technologies such as the FlagScale, together with vllm-plugin-fl, distributed training/inference framework, FlagGems universal operator library, FlagCX communication library, and FlagTree unified compiler, the FlagRelease platform leverages the FlagOS stack to automatically produce and release various combinations of <chip + open-source model>. This enables efficient and automated model migration across diverse chips, opening a new chapter for large model deployment and application.
FlagGems
FlagGems is a high-performance, generic operator libraryimplemented in Triton language. It is built on a collection of backend-neutralkernels that aims to accelerate LLM (Large-Language Models) training and inference across diverse hardware platforms.
FlagTree
FlagTree is an open source, unified compiler for multipleAI chips project dedicated to developing a diverse ecosystem of AI chip compilers and related tooling platforms, thereby fostering and strengthening the upstream and downstream Triton ecosystem. Currently in its initial phase, the project aims to maintain compatibility with existing adaptation solutions while unifying the codebase to rapidly implement single-repository multi-backend support. Forupstream model users, it provides unified compilation capabilities across multiple backends; for downstream chip manufacturers, it offers examples of Triton ecosystem integration.
FlagScale and vllm-plugin-fl
Flagscale is a comprehensive toolkit designed to supportthe entire lifecycle of large models. It builds on the strengths of several prominent open-source projects, including Megatron-LM and vLLM, to provide a robust, end-to-end solution for managing and scaling large models.
vllm-plugin-fl is a vLLM plugin built on the FlagOS unified multi-chip backend, to help flagscale support multi-chip on vllm framework.
FlagCX
FlagCX is a scalable and adaptive cross-chip communication library. It serves as a platform where developers, researchers, and AI engineers can collaborate on various projects, contribute to the development of cutting-edge AI solutions, and share their work with the global community.
FlagEval Evaluation Framework
FlagEval is a comprehensive evaluation system and open platform for large models launched in 2023. It aims to establish scientific, fair, and open benchmarks, methodologies, and tools to help researchers assess model and training algorithm performance. It features:
Multi-dimensional Evaluation: Supports 800+ modelevaluations across NLP, CV, Audio, and Multimodal fields,covering 20+ downstream tasks including language understanding and image-text generation.
Industry-Grade Use Cases: Has completed horizonta1 evaluations of mainstream large models, providing authoritative benchmarks for chip-model performance validation.
