Inkling

Description

Inkling is a general-purpose multimodal autoregressive transformer model from Thinking Machines Lab Inc. that accepts text, image, and audio inputs and generates text outputs. The model is intended for English and other languages, multiple coding languages, agentic and tool-use systems, coding assistants, chatbots, retrieval-augmented generation systems, general-purpose conversational use, instruction following, and other natural language or multimodal tasks.

This model is ready for commercial or non-commercial use.

Third-Party Community Consideration:

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA Inkling Model Card

License and Terms of Use:

**GOVERNING TERMS: Use of the model is governed by the NVIDIA Open Model Agreement. ADDITIONAL INFORMATION: Apache License, Version 2.0.

Deployment Geography:

Global

Use Case:

Use Case: Inkling is intended for developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, retrieval-augmented generation systems, general-purpose conversational AI, instruction-following systems, and multimodal applications.

Release Date:

Build.NVIDIA.com: 07/15/2026 via link\nHuggingface: 07/15/2026 via link

Reference(s):

References:

Model Architecture:

Architecture Type: Transformer\nNetwork Architecture: Mixture-of-Experts\nTotal Parameters: 975B\nActive Parameters: 41B

Input:

Input Types: Text, Image, Audio\nInput Formats: String, Red, Green, Blue (RGB), Other (Waveform)\nInput Parameters: One-Dimensional (1D), Two-Dimensional (2D), One-Dimensional (1D)\nOther Input Properties: Text input uses UTF-8 encoding; image input supports pixel-based formats with each dimension ideally between 40 and 4,096 pixels; audio input uses WAV format sampled at 16 kHz and is ideally under 20 minutes.

Output:

Output Types: Text\nOutput Format: String\nOutput Parameters: One-Dimensional (1D)\nOther Output Properties: Inkling generates UTF-8 encoded text.

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.

Software Integration:

Runtime Engines:

SGLang
vLLM

Supported Hardware:

NVIDIA Blackwell: B300
NVIDIA Hopper: H200

Preferred Operating Systems: Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s)

Inkling v1.0

Training, Testing, and Evaluation Datasets:

Training Dataset

Data Modality: Text, Image, Audio, Video\nText Training Data Size: Undisclosed\nImage Training Data Size: Undisclosed\nAudio Training Data Size: Undisclosed\nVideo Training Data Size: Undisclosed\nTraining Data Collection: Hybrid: Automated, Manually-Collected, Synthetic\nTraining Labeling: Undisclosed\nTraining Properties: Training data includes a broad variety of content types, including text, images, audio, and video, drawn from publicly available sources, third-party acquired sources, and synthetically generated or augmented data. The training data curation process includes cleaning, processing, modification, deduplication, filtering, and other data-type-specific processing steps to remove low-quality content or advance safety objectives.

Testing Dataset

Testing Data Collection: Undisclosed\nTesting Labeling: Undisclosed\nTesting Properties: Undisclosed

Evaluation Dataset

Evaluation Benchmark Score: Inkling reports reasoning, agentic, factuality, chat, vision, audio, and safety benchmark results at effort=0.99. Selected official Hugging Face scores include 46.0% HLE with tools, 97.1% AIME 2026, 77.6% SWEBench Verified, 54.3% SWEBench Pro (Public), 63.8 Terminal Bench 2.1, 88.7% Global-MMLU-Lite, 73.3% MMMU Pro (Standard 10), 91.4% VoiceBench, and 98.6% StrongREJECT.

Detailed benchmark comparison tables are available in the official Inkling Model Card.

Evaluation Data Collection: Hybrid: Automated, Manually-Collected\nEvaluation Labeling: Hybrid: Automated, Manually-Labeled\nEvaluation Properties: The evaluation suite covers reasoning, coding and agentic tasks, factuality, chat and instruction following, multimodal vision and audio tasks, and safety benchmarks, with results reported at effort=0.99 and comparison scores from the r/acc leaderboard generated July 14, 2026.

Inference

Acceleration Engine: SGLang + Dynamo\nTest Hardware: NVIDIA Blackwell (GB200)

Additional Details

Inkling may exhibit general limitations common to foundation models, including hallucination, occasional failures to follow instructions precisely, degraded performance in long multi-turn conversations, demographic, cultural, or linguistic bias, and uneven performance across languages, dialects, or subject domains that were less represented during training. Downstream developers and deployers should apply human oversight for high-stakes or safety-critical contexts, conduct use-case-specific evaluation, and implement application-layer safeguards such as content filtering, rate limiting, monitoring, and input/output moderation.

Safety evaluations were conducted ahead of release across everyday human-AI interaction and dangerous-capability testing. Evaluation areas included sycophancy, harmful manipulation, psychological-harm patterns, refusal behavior, CBRN, cyber, loss of control, agentic capability, strategic deception, sabotage potential, internal evaluations, external testing, open-ended external red teaming, and refusal-suppressed variants to estimate latent capability with safeguards removed. For more information on model safety, please view the Inkling Model Card.

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 model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please make sure you have proper rights and permissions for all input image content; if image includes people, personal health information, or intellectual property, the image generated will not blur or maintain proportions of image subjects included.

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

Get Help

NVIDIA Developer Community Forum

Get access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)

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The model is intended for English and other languages, multiple coding languages, agentic and tool-use systems, coding assistants, chatbots, retrieval-augmented generation systems, general-purpose conversational use, instruction following, and other natural language or multimodal tasks.\n\n*This model is ready for commercial or non-commercial use.*\n\n## Third-Party Community Consideration:\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA [Inkling Model Card](https://huggingface.co/thinkingmachines/Inkling)\n\n## License and Terms of Use:\n**GOVERNING TERMS: 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: [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n\n## Deployment Geography:\nGlobal\n\n## Use Case:\n**Use Case:** Inkling is intended for developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, retrieval-augmented generation systems, general-purpose conversational AI, instruction-following systems, and multimodal applications.\n\n## Release Date:\n**Build.NVIDIA.com:** 07/15/2026 via [link](http://build.nvidia.com/thinkingmachines/inkling)\n**Huggingface:** 07/15/2026 via [link](https://huggingface.co/thinkingmachines/Inkling)\n\n## Reference(s):\n**References:**\n- [Inkling Model Page](https://huggingface.co/thinkingmachines/Inkling)\n- [Inkling NVFP4 Model Page](https://huggingface.co/thinkingmachines/Inkling-NVFP4)\n- [Thinking Machines Acceptable Use Policy](https://thinkingmachines.ai/model-acceptable-use-policy/)\n- [Tinker Cookbook](https://github.com/thinking-machines-lab/tinker-cookbook)\n- [Inkling Tinker Documentation](https://tinker-docs.thinkingmachines.ai/cookbook/inkling/)\n\n## Model Architecture:\n**Architecture Type:** Transformer\n**Network Architecture:** Mixture-of-Experts\n**Total Parameters:** 975B\n**Active Parameters:** 41B\n\n### Input:\n**Input Types:** Text, Image, Audio\n**Input Formats:** String, Red, Green, Blue (RGB), Other (Waveform)\n**Input Parameters:** One-Dimensional (1D), Two-Dimensional (2D), One-Dimensional (1D)\n**Other Input Properties:** Text input uses UTF-8 encoding; image input supports pixel-based formats with each dimension ideally between 40 and 4,096 pixels; audio input uses WAV format sampled at 16 kHz and is ideally under 20 minutes.\n\n### Output:\n**Output Types:** Text\n**Output Format:** String\n**Output Parameters:** One-Dimensional (1D)\n**Other Output Properties:** Inkling generates UTF-8 encoded text.\n\n__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.__\n\n## Software Integration:\n**Runtime Engines:**\n- **SGLang**\n- **vLLM**\n\n**Supported Hardware:**\n- **NVIDIA Blackwell:** B300\n- **NVIDIA Hopper:** H200\n\n**Preferred Operating Systems:** Linux\n\n__The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.__\n\n## Model Version(s)\nInkling v1.0\n\n## Training, Testing, and Evaluation Datasets:\n\n### Training Dataset\n**Data Modality:** Text, Image, Audio, Video\n**Text Training Data Size:** Undisclosed\n**Image Training Data Size:** Undisclosed\n**Audio Training Data Size:** Undisclosed\n**Video Training Data Size:** Undisclosed\n**Training Data Collection:** Hybrid: Automated, Manually-Collected, Synthetic\n**Training Labeling:** Undisclosed\n**Training Properties:** Training data includes a broad variety of content types, including text, images, audio, and video, drawn from publicly available sources, third-party acquired sources, and synthetically generated or augmented data. The training data curation process includes cleaning, processing, modification, deduplication, filtering, and other data-type-specific processing steps to remove low-quality content or advance safety objectives.\n\n### Testing Dataset\n**Testing Data Collection:** Undisclosed\n**Testing Labeling:** Undisclosed\n**Testing Properties:** Undisclosed\n\n### Evaluation Dataset\n**Evaluation Benchmark Score:** Inkling reports reasoning, agentic, factuality, chat, vision, audio, and safety benchmark results at effort=0.99. Selected official Hugging Face scores include 46.0% HLE with tools, 97.1% AIME 2026, 77.6% SWEBench Verified, 54.3% SWEBench Pro (Public), 63.8 Terminal Bench 2.1, 88.7% Global-MMLU-Lite, 73.3% MMMU Pro (Standard 10), 91.4% VoiceBench, and 98.6% StrongREJECT.\n\nDetailed benchmark comparison tables are available in the official [Inkling Model Card](https://huggingface.co/thinkingmachines/Inkling).\n\n**Evaluation Data Collection:** Hybrid: Automated, Manually-Collected\n**Evaluation Labeling:** Hybrid: Automated, Manually-Labeled\n**Evaluation Properties:** The evaluation suite covers reasoning, coding and agentic tasks, factuality, chat and instruction following, multimodal vision and audio tasks, and safety benchmarks, with results reported at effort=0.99 and comparison scores from the r/acc leaderboard generated July 14, 2026.\n\n## Inference\n**Acceleration Engine:** SGLang + Dynamo\n**Test Hardware:** NVIDIA Blackwell (GB200)\n\n## Additional Details\nInkling may exhibit general limitations common to foundation models, including hallucination, occasional failures to follow instructions precisely, degraded performance in long multi-turn conversations, demographic, cultural, or linguistic bias, and uneven performance across languages, dialects, or subject domains that were less represented during training. Downstream developers and deployers should apply human oversight for high-stakes or safety-critical contexts, conduct use-case-specific evaluation, and implement application-layer safeguards such as content filtering, rate limiting, monitoring, and input/output moderation.\n\nSafety evaluations were conducted ahead of release across everyday human-AI interaction and dangerous-capability testing. Evaluation areas included sycophancy, harmful manipulation, psychological-harm patterns, refusal behavior, CBRN, cyber, loss of control, agentic capability, strategic deception, sabotage potential, internal evaluations, external testing, open-ended external red teaming, and refusal-suppressed variants to estimate latent capability with safeguards removed. For more information on model safety, please view the [Inkling Model Card](https://huggingface.co/thinkingmachines/Inkling).\n\n## Ethical Considerations\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 model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nPlease make sure you have proper rights and permissions for all input image content; if image includes people, personal health information, or intellectual property, the image generated will not blur or maintain proportions of image subjects included.\n\nPlease report model 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### NVIDIA Developer Community Forum\nGet access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)","displayName","Inkling",["D",1784156710460],"publisher","Thinking Machines Lab","latestVersion","collectionCounts","imageUrl","/api/image/nim/thinkingmachines/models/inkling","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms",{"_257":258,"_259":260,"_261":262,"_263":34,"_264":265},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[251],"displayLabels",[240,241],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_209":249,"_243":249,"_244":250,"_246":247},{"_209":242,"_243":242,"_244":245,"_246":247},"NSPECT-AS16-OTEF","value","filter",{"_248":247,"_243":242},"category","label","field","False",{"_248":247,"_243":249},{"_209":252,"_243":253,"_244":254,"_246":255},"NVIDIA NIM","nimmcro_nvidia_nim",{"_248":256,"_243":253},"feature","nvidia_nim","governingTerms","

GOVERNING DOWNLOAD TERMS: Use of the model is governed by the NVIDIA Open Model Agreement.

ADDITIONAL INFORMATION: Apache License, Version 2.0.

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GOVERNING DOWNLOAD TERMS: Use of the model is governed by the NVIDIA Open Model Agreement.

ADDITIONAL INFORMATION: Apache License, Version 2.0.

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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. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.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.","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. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed 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.","_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. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","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.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. 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