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",1787295749657],"publisher","Thinking Machines Lab","latestVersion","collectionCounts","imageUrl","/api/image/nim/thinkingmachines/models/inkling","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms",{"_277":278,"_279":280,"_281":282,"_283":35,"_284":285},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[271],"displayLabels",[260,261],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_229":269,"_263":269,"_264":270,"_266":267},{"_229":262,"_263":262,"_264":265,"_266":267},"NSPECT-AS16-OTEF","value","filter",{"_268":267,"_263":262},"category","label","field","False",{"_268":267,"_263":269},{"_229":272,"_263":273,"_264":274,"_266":275},"NVIDIA NIM","nimmcro_nvidia_nim",{"_268":276,"_263":273},"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 tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638],[585,586,587,588,589],{"_423":498,"_229":498,"_195":196,"_396":609},{"_423":472,"_229":472,"_195":196,"_396":604},{"_423":599,"_229":599,"_195":196,"_396":600},{"_423":594,"_229":594,"_195":196,"_396":595},{"_423":590,"_229":590,"_195":196,"_396":591},"nvidia.partners",[592],{"_81":593,"_429":590,"_430":35,"_195":466},"edge","app.hushly.com",[596],{"_81":597,"_429":594,"_430":35,"_195":598},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[601],{"_81":602,"_429":599,"_430":35,"_195":603},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725],[677,678,679,680],{"_423":692,"_229":692,"_195":196,"_396":693},{"_423":687,"_229":687,"_195":196,"_396":688},{"_423":485,"_229":485,"_195":196,"_396":684},{"_423":599,"_229":599,"_195":196,"_396":681},[682],{"_81":683,"_429":599,"_430":92,"_195":603},"_gh_sess",[685],{"_81":686,"_429":485,"_430":92,"_195":492},"YSC","bizible.com",[689],{"_81":690,"_429":687,"_430":35,"_195":691},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[694],{"_81":695,"_429":692,"_430":35,"_195":696},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_81":767,"_429":544,"_430":35,"_195":768},{"_81":752,"_429":766,"_430":92,"_195":753},{"_81":735,"_429":547,"_430":35,"_195":660},{"_81":735,"_429":648,"_430":35,"_195":660},{"_81":764,"_429":544,"_430":35,"_195":765},{"_81":752,"_429":550,"_430":92,"_195":753},{"_81":762,"_429":544,"_430":35,"_195":763},{"_81":752,"_429":547,"_430":92,"_195":753},{"_81":760,"_429":647,"_430":35,"_195":761},{"_81":752,"_429":669,"_430":92,"_195":753},{"_81":732,"_429":547,"_430":35,"_195":734},{"_81":758,"_429":544,"_430":92,"_195":759},{"_81":756,"_429":544,"_430":35,"_195":757},{"_81":754,"_429":544,"_430":92,"_195":755},{"_81":752,"_429":643,"_430":92,"_195":753},{"_81":750,"_429":544,"_430":35,"_195":751},{"_81":748,"_429":544,"_430":35,"_195":749},{"_81":746,"_429":544,"_430":92,"_195":747},{"_81":744,"_429":652,"_430":35,"_195":745},{"_81":732,"_429":643,"_430":35,"_195":734},{"_81":742,"_429":544,"_430":35,"_195":743},{"_81":740,"_429":544,"_430":92,"_195":741},{"_81":738,"_429":544,"_430":35,"_195":739},{"_81":736,"_429":544,"_430":35,"_195":737},{"_81":735,"_429":544,"_430":35,"_195":660},{"_81":732,"_429":733,"_430":35,"_195":734},{"_81":730,"_429":544,"_430":35,"_195":731},{"_81":728,"_429":544,"_430":92,"_195":729},{"_81":726,"_429":544,"_430":35,"_195":727},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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