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GOVERNING DOWNLOAD TERMS: Use of this container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for NVIDIA AI Products.

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GLM-5.2 NIM Overview

Description:

This container houses GLM-5.2, a flagship long-horizon reasoning and coding model from Z.AI. GLM-5.2 improves on GLM-5.1 with a solid 1M-token context window, stronger coding performance, flexible thinking effort levels, and architecture improvements for sparse attention and speculative decoding.

The GLM-5.2 NIM is distributed through NGC and runs with the NVIDIA NIM for Large Language Models runtime using SGLang-backed profiles for supported NVIDIA GPU-accelerated systems.

The container components are ready for commercial use.

Third-Party Community Consideration

The model embedded in the container is not owned or developed by NVIDIA. Please see link to Non-NVIDIA GLM-5.2 Model Card and GLM-5 GitHub repository.

License/Terms of Use:

Governing Terms: The NIM container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for NVIDIA AI Products. Use of the model is governed by the NVIDIA Open Model Agreement. Additional Information: MIT License.

You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.

NVIDIA Legal Release Process provides instructions for getting legal support for license selection.

Deployment Geography:

Global

Release Date:

NGC: 06/30/2026 via NVIDIA NGC GLM-5.2 container page

Program Classes:

GLM-5.2 Container includes the following model:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model Name & Link	Use Case	How to Pull the Model
GLM-5.2	GLM-5.2 is a long-horizon reasoning and coding model for advanced coding, tool use, terminal operations, repository-scale generation, and multi-step agentic workflows.	Automatic

Deployment Details:

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

GLM-5.2 is deployed as a Downloadable NIM container from NGC. Users pull the container image from NGC, configure the service following the NGC container page and NVIDIA NIM documentation, and integrate the standard inference APIs into downstream applications and tools.

Supported Hardware Microarchitecture Compatibility:\nGPUs: 8 NVIDIA B200 Tensor Core GPUs, 8 NVIDIA H20 Tensor Core GPUs, 8 NVIDIA H200 Tensor Core GPUs, or compatible systems with more than 900 GB of aggregate GPU memory.\nCPU: x86_64 host CPU compatible with NVIDIA NIM for Large Language Models deployments.\nMemory: Host memory sized for the selected NIM deployment profile and workload concurrency.\nStorage: At least 736 GB of disk space for the GLM-5.2 NIM profile.

Reference(s):

Container Version(s):

GLM-5.2 NIM v1.11.0-variant - base Downloadable NIM container release.

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse.

Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Get Help

Getting started with the NIM

Deploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the NVIDIA NIM documentation for release documentation, deployment guides and more.

NVIDIA Developer Community Forum

For support, visit the NVIDIA Developer Community Forum.

","resourceVersions",[149,150,151],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_123":95,"_124":-5,"_41":125,"_126":-5,"_127":128,"_129":130,"_131":-5,"_132":-5,"_133":16,"_134":135,"_136":18,"_137":-5,"_138":18,"_139":-5,"_140":18,"_141":-5,"_142":18,"_143":-5,"_144":18,"_145":-5,"_146":18,"_147":148},"version","latest","imageReference","nvcr.io/nim/zai-org/glm-5.2:latest","cliDownloadCommand","helmFetchCommand","viewAllVersionsPath","/orgs/nim/zai-org/containers/glm-5.2/latest/tags","accessCheckResult",{"_122":16},"hasRelatedCollections","context",{"_118":119,"_120":121},1782915155146,[],[],[111,112,113,114,115,116],{"_37":57,"_39":62,"_41":54,"_43":55,"_45":61,"_47":48},{"_37":57,"_39":60,"_41":54,"_43":55,"_45":61,"_47":48},{"_37":57,"_39":58,"_41":54,"_43":55,"_45":117,"_47":48},{"_37":52,"_39":53,"_41":54,"_43":55,"_45":56,"_47":48},{"_37":49,"_39":50,"_41":42,"_43":44,"_45":51,"_47":48},{"_37":38,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48},165,"orgName","nim","teamName","zai-org","allowed","versionId","description","NOT_SCANNED","releaseType","createdDate",["D",1782847553821],"updatedDate",["D",1782847553821],"totalSizeInBytes","totalFileCount","isSigned","malwareScanStatus","IN_PROGRESS","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_152":157,"_153":158,"_126":-5,"_133":16,"_136":18,"_147":156,"_138":18},{"_152":155,"_153":155,"_126":-5,"_133":16,"_136":18,"_147":156,"_138":18},{"_152":95,"_153":95,"_126":-5,"_133":16,"_136":18,"_147":154,"_138":18},"id","name",[],"1.11.0-variant",[],"-","Latest (1.11.0-variant)","nim/zai-org/glm-5.2","nim/zai-org/containers/glm-5.2","resourceType","containers","resourceId","glm-5.2","shortDescription","This container houses GLM-5.2, a flagship long-context large language model for agentic engineering and advanced reasoning, packaged as an NVIDIA NIM for staging.","# GLM-5.2 NIM Overview\n\n## Description:\nThis container houses **GLM-5.2**, a flagship long-horizon reasoning and coding model from Z.AI. GLM-5.2 improves on GLM-5.1 with a solid 1M-token context window, stronger coding performance, flexible thinking effort levels, and architecture improvements for sparse attention and speculative decoding.\n\nThe GLM-5.2 NIM is distributed through NGC and runs with the NVIDIA NIM for Large Language Models runtime using SGLang-backed profiles for supported NVIDIA GPU-accelerated systems.\n\n*The container components are ready for commercial use.*\n\n## Third-Party Community Consideration\nThe model embedded in the container is not owned or developed by NVIDIA. Please see link to Non-NVIDIA [GLM-5.2 Model Card](https://huggingface.co/zai-org/GLM-5.2) and [GLM-5 GitHub repository](https://github.com/zai-org/GLM-5).\n\n## License/Terms of Use:\n\n**Governing Terms:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/). Use of the model is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/). Additional Information: [MIT License](https://github.com/zai-org/GLM-5).\n\n**You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.**\n\n[NVIDIA Legal Release Process](https://nvidia.sharepoint.com/sites/ProductLegalSupport) provides instructions for getting legal support for license selection.\n\n## Deployment Geography:\n\nGlobal\n\n## Release Date:\n\n**NGC:** 06/30/2026 via [NVIDIA NGC GLM-5.2 container page](https://catalog.ngc.nvidia.com/orgs/nim/teams/zai-org/containers/glm-5.2)\n\n## Program Classes:\n\nGLM-5.2 Container includes the following model:\n\n| Model Name & Link | Use Case | How to Pull the Model |\n| :---- | :---- | :---- |\n| [GLM-5.2](https://catalog.ngc.nvidia.com/orgs/nim/teams/zai-org/models/glm-52) | GLM-5.2 is a long-horizon reasoning and coding model for advanced coding, tool use, terminal operations, repository-scale generation, and multi-step agentic workflows. | Automatic |\n\n## Deployment Details:\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\nGLM-5.2 is deployed as a Downloadable NIM container from NGC. Users pull the container image from NGC, configure the service following the NGC container page and NVIDIA NIM documentation, and integrate the standard inference APIs into downstream applications and tools.\n\nSupported Hardware Microarchitecture Compatibility:\nGPUs: 8 NVIDIA B200 Tensor Core GPUs, 8 NVIDIA H20 Tensor Core GPUs, 8 NVIDIA H200 Tensor Core GPUs, or compatible systems with more than 900 GB of aggregate GPU memory.\nCPU: x86_64 host CPU compatible with NVIDIA NIM for Large Language Models deployments.\nMemory: Host memory sized for the selected NIM deployment profile and workload concurrency.\nStorage: At least 736 GB of disk space for the GLM-5.2 NIM profile.\n\n## Reference(s):\n\n* [GLM-5 GitHub repository](https://github.com/zai-org/GLM-5)\n* [GLM-5.2 model page](https://huggingface.co/zai-org/GLM-5.2)\n* [Z.AI GLM-5.2 blog](https://z.ai/blog/glm-5.2)\n* [Z.AI GLM-5.2 developer documentation](https://docs.z.ai/guides/llm/glm-5.2)\n* [NVIDIA NIM for Large Language Models documentation](https://docs.nvidia.com/nim/large-language-models/latest/about-nim-llm/overview.html)\n\n## Container Version(s):\n\nGLM-5.2 NIM v1.11.0-variant - base Downloadable NIM container release.\n\n\n## Ethical Considerations\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse.\n\nPlease report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## Get Help\n\n### Getting started with the NIM\n\nDeploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the [NVIDIA NIM documentation](https://docs.nvidia.com/nim/large-language-models/latest/about-nim-llm/overview.html) for release documentation, deployment guides and more.\n\n### NVIDIA Developer Community Forum\n\nFor support, visit the [NVIDIA Developer Community Forum](https://forums.developer.nvidia.com/).","displayName",["D",1782847554752],"publisher","latestVersion","versions",[155,95],"imageUrl","/api/image/nim/zai-org/containers/glm-5.2","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",11654434550,"licenseTerms",{"_220":221,"_222":14,"_223":224,"_225":18,"_226":227},"isMultinodeEnabled","isMultiArchitecture","hasSignedTag","featureLabels",[215],"displayLabels",[203,204,205,206],"policyLabels",[],"systemLabels",[196],"products",[],"hasNimMetadata",{"_168":197,"_198":197,"_199":200,"_72":201},"signed images","value","filter",{"_202":201,"_198":197},"system","field",{"_168":213,"_198":213,"_199":214,"_72":78},{"_168":211,"_198":211,"_199":212,"_72":78},{"_168":209,"_198":209,"_199":210,"_72":78},{"_168":207,"_198":207,"_199":208,"_72":78},"NSPECT-MM87-K0I7",{"_202":78,"_198":207},"nimmcro_nvidia_nim",{"_202":78,"_198":209},"LLM",{"_202":78,"_198":211},"Experimental",{"_202":78,"_198":213},{"_168":216,"_198":209,"_199":217,"_72":218},"NVIDIA NIM",{"_202":219,"_198":209},"feature","nvidia_nim","governingTerms","

GOVERNING DOWNLOAD TERMS: Use of this container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for NVIDIA AI Products.

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This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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