Inkling-Small

Description

Inkling-Small is a general-purpose multimodal autoregressive transformer that accepts text, image, and audio inputs and generates text output. It is intended for use across English and other languages, as well as multiple programming languages.

The model is designed for developers building agentic and tool-use systems, coding assistants, chatbots, retrieval-augmented generation systems, instruction-following applications, and other natural-language or multimodal workflows. It is released with open weights to support research, fine-tuning, and integration into third-party products.

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 the non-NVIDIA Thinking Machines Lab Inkling-Small Model Card.

License and Terms of Use

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

ADDITIONAL INFORMATION: Apache License 2.0.

Deployment Geography

Global

Use Case

Developers can use Inkling-Small to build multimodal assistants, agentic and tool-use systems, coding assistants, chatbots, retrieval-augmented generation systems, and general conversational or instruction-following applications that accept text, image, or audio inputs and produce text.

Inkling-Small is intended for research, fine-tuning, and integration into third-party products. Downstream developers should perform use-case-specific evaluation and apply appropriate safeguards, monitoring, and human oversight before deployment, particularly in high-stakes or safety-critical applications.

Release Date

[Hugging Face] 07/27/2026 via Inkling-Small on Hugging Face.\n[NGC Catalog] 11/05/2026 via Inkling-Small

References

Model Architecture

Architecture Type: Transformer
\nNetwork Architecture: Multimodal autoregressive, decoder-only sparse Mixture-of-Experts (MoE) transformer
\nNumber of Model Parameters: 276B total, 12B active
\nNumber of Layers: 42
\nNumerics Support: BF16 and NVFP4

Inkling-Small uses a sparse MoE feed-forward backbone. Each token is routed to 6 of 256 experts, with 2 additional shared experts active for every token. The attention stack combines local and global layers. Images are processed using a hierarchical patch encoder, while audio is represented using discrete token encoding. All supported modalities are projected into a shared hidden space and processed jointly by the decoder.

Input

Input Type(s): Text, Image, Audio\nInput Format(s): UTF-8 text, Red, Green, Blue (RGB), WAV audio
\nInput Parameters: One-Dimensional (1D), Two-Dimensional (2D), and One-Dimensional (1D)\nOther Properties Related to Input: For optimal performance, each image dimension should be between 40 and 4,096 pixels. Audio should use WAV format sampled at 16 kHz and should ideally be shorter than two minutes.

Output

Output Type(s): Text
\nOutput Format: UTF-8 encoded string
\nOutput Parameters: One-Dimensional (1D) sequences
\nOther Properties Related to Output: The model produces natural-language and code output.

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 B200, NVIDIA Blackwell B300, NVIDIA Blackwell GB300, NVIDIA Blackwell GB200
NVIDIA Hopper H100, NVIDIA Hopper H200, NVIDIA Hopper H100 NVL

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-Small BF16
Inkling-Small NVFP4

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
\nData Collection Method by Dataset: Hybrid: Automated, Manually-Collected, Synthetic .
\nLabeling Method by Dataset: Undisclosed
\nProperties: The data-curation process included cleaning, processing, and modifying datasets. Depending on data type, processing could include deduplication and filtering to remove junk or other low-quality content or to advance safety and other objectives.

Testing Dataset

Data Collection Method by Dataset: Undisclosed
\nLabeling Method by Dataset: Undisclosed
\nProperties: Undisclosed

Evaluation Dataset

Evaluation Benchmark Score: The Inkling-Small source model card reports results across agentic coding, general agentic performance, reasoning, factuality, chat, safety, vision, and audio. Selected reported Inkling-Small results include:

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

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

Category	Benchmark	Reported Score
Overall	AA Index v4.1	40.0%
Agentic Coding	SWE-Bench Verified	80.2%
Agentic Coding	SWE-Bench Pro (public)	55.9%
Agentic Coding	Terminal Bench 2.1	64.7%
General Agentic	MCP Atlas (public/all)	79.6% / 79.2%
General Agentic	Toolathlon Verified	54.4%
General Reasoning	GPQA Diamond	89.5%
General Reasoning	AIME 2026	95.5%
Abstract Reasoning	ARC-AGI-1	84.0%
Abstract Reasoning	ARC-AGI-2	40.1%
Chat	IFBench	82.2%
Vision	MMMU Pro (Standard 10)	74.0%
Audio	MMAU	77.0%
Audio	VoiceBench	90.1%

Data Collection Method by Dataset: Hybrid: Automated, Manually-Collected.
\nLabeling Method by Dataset: Undisclosed
\nProperties: Evaluation covers coding agents, tool use, general and abstract reasoning, factuality, instruction following, multilingual knowledge, safety, vision understanding, and audio understanding. See the linked third-party model card for the complete comparison tables, harness notes, and methodology. NVIDIA has not independently reproduced the linked results.

Inference

Acceleration Engine: SGLang and vLLM are supported through public deployment recipes.
\nTest Hardware: NNVIDIA Blackwell (GB200, B200, B300, GB300), NVIDIA Hopper (H200)

Known Limitations

Inkling-Small may exhibit limitations common to foundation models, including hallucinations, occasional failures to follow instructions precisely, and degraded performance in long, multi-turn conversations. The model may reflect demographic, cultural, or linguistic biases in its web-derived and synthetic training data and may perform unevenly across languages, dialects, or subject areas that were less represented during training.

The model's knowledge is limited to information available as of its training cutoff, which is not specified in the source model card. It may not reflect later events or developments.

Downstream developers should:

Evaluate performance, safety, and fairness for their specific use case, language, and population before deployment.
Apply content filtering, rate limiting, monitoring, and other application-layer safeguards.
Use human review for high-stakes or safety-critical outputs.
Avoid medical, legal, or other safety-critical decision-making deployments without additional fine-tuning, domain-specific validation, and human oversight.

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 and video content; if image or video includes people, personal health information, or intellectual property, the image or video 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

Enterprise Support

For help with NVIDIA NIM, customers with an active NVIDIA AI Enterprise subscription can file a support ticket.

NVIDIA Developer Community Forum

Access community knowledge-base articles and support cases through the NVIDIA Developer Forums.

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It is intended for use across English and other languages, as well as multiple programming languages.\n\nThe model is designed for developers building agentic and tool-use systems, coding assistants, chatbots, retrieval-augmented generation systems, instruction-following applications, and other natural-language or multimodal workflows. It is released with open weights to support research, fine-tuning, and integration into third-party products.\n\nThis model is ready for commercial or non-commercial use.\n\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 the non-NVIDIA Thinking Machines Lab [Inkling-Small Model Card](https://huggingface.co/thinkingmachines/Inkling-Small).\n\n\n## License and Terms of Use\n\n**GOVERNING DOWNLOAD TERMS:** Use of this model is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/).\n\n**ADDITIONAL INFORMATION:** [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). \n\n## Deployment Geography\n\nGlobal\n\n## Use Case\n\nDevelopers can use Inkling-Small to build multimodal assistants, agentic and tool-use systems, coding assistants, chatbots, retrieval-augmented generation systems, and general conversational or instruction-following applications that accept text, image, or audio inputs and produce text.\n\nInkling-Small is intended for research, fine-tuning, and integration into third-party products. Downstream developers should perform use-case-specific evaluation and apply appropriate safeguards, monitoring, and human oversight before deployment, particularly in high-stakes or safety-critical applications.\n\n## Release Date\n[Hugging Face] 07/27/2026 via [Inkling-Small on Hugging Face](https://huggingface.co/thinkingmachines/Inkling-Small).\n[NGC Catalog] 11/05/2026 via [Inkling-Small](https://catalog.ngc.nvidia.com/orgs/nim/thinkingmachines/containers/inkling-small/-)\n\n## References\n\n- [Inkling-Small on Hugging Face](https://huggingface.co/thinkingmachines/Inkling-Small)\n- [Inkling-Small BF16 weights](https://huggingface.co/thinkingmachines/Inkling-Small)\n- [Inkling-Small NVFP4 weights](https://huggingface.co/thinkingmachines/Inkling-Small-NVFP4)\n- [Tinker Playground](https://tinker.thinkingmachines.ai/playground)\n- [Tinker Cookbook](https://github.com/thinking-machines-lab/tinker-cookbook)\n- [SGLang recipe](https://docs.sglang.io/cookbook/autoregressive/ThinkingMachines/Inkling-Small)\n- [vLLM recipe](https://recipes.vllm.ai/thinkingmachines/Inkling-Small)\n- [Training Data Documentation](https://thinkingmachines.ai/training-data-documentation/)\n\n## Model Architecture\n\n**Architecture Type:** Transformer \n**Network Architecture:** Multimodal autoregressive, decoder-only sparse Mixture-of-Experts (MoE) transformer \n**Number of Model Parameters:** 276B total, 12B active \n**Number of Layers:** 42 \n**Numerics Support:** BF16 and NVFP4\n\nInkling-Small uses a sparse MoE feed-forward backbone. Each token is routed to 6 of 256 experts, with 2 additional shared experts active for every token. The attention stack combines local and global layers. Images are processed using a hierarchical patch encoder, while audio is represented using discrete token encoding. All supported modalities are projected into a shared hidden space and processed jointly by the decoder.\n\n### Input\n\n**Input Type(s):** Text, Image, Audio\n**Input Format(s):** UTF-8 text, Red, Green, Blue (RGB), WAV audio \n**Input Parameters:** One-Dimensional (1D), Two-Dimensional (2D), and One-Dimensional (1D)\n**Other Properties Related to Input:** For optimal performance, each image dimension should be between 40 and 4,096 pixels. Audio should use WAV format sampled at 16 kHz and should ideally be shorter than two minutes.\n\n### Output\n\n**Output Type(s):** Text \n**Output Format:** UTF-8 encoded string \n**Output Parameters:** One-Dimensional (1D) sequences \n**Other Properties Related to Output:** The model produces natural-language and code output.\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\n## Software Integration:\n**Runtime Engines:**\n- **SGLang**\n- **vLLM**\n\n**Supported Hardware:**\n- **NVIDIA Blackwell B200, NVIDIA Blackwell B300, NVIDIA Blackwell GB300, NVIDIA Blackwell GB200** \n- **NVIDIA Hopper H100, NVIDIA Hopper H200, NVIDIA Hopper H100 NVL**\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)\n\n- Inkling-Small BF16\n- Inkling-Small NVFP4\n\n\n## Training, Testing, and Evaluation Datasets\n\n### Training Dataset\n\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**Data Collection Method by Dataset:** Hybrid: Automated, Manually-Collected, Synthetic . \n**Labeling Method by Dataset:** Undisclosed \n**Properties:** The data-curation process included cleaning, processing, and modifying datasets. Depending on data type, processing could include deduplication and filtering to remove junk or other low-quality content or to advance safety and other objectives.\n\n### Testing Dataset\n\n**Data Collection Method by Dataset:** Undisclosed \n**Labeling Method by Dataset:** Undisclosed \n**Properties:** Undisclosed\n\n### Evaluation Dataset\n\n**Evaluation Benchmark Score:** The Inkling-Small source model card reports results across agentic coding, general agentic performance, reasoning, factuality, chat, safety, vision, and audio. Selected reported Inkling-Small results include:\n\n| Category | Benchmark | Reported Score |\n| --- | --- | ---: |\n| Overall | AA Index v4.1 | 40.0% |\n| Agentic Coding | SWE-Bench Verified | 80.2% |\n| Agentic Coding | SWE-Bench Pro (public) | 55.9% |\n| Agentic Coding | Terminal Bench 2.1 | 64.7% |\n| General Agentic | MCP Atlas (public/all) | 79.6% / 79.2% |\n| General Agentic | Toolathlon Verified | 54.4% |\n| General Reasoning | GPQA Diamond | 89.5% |\n| General Reasoning | AIME 2026 | 95.5% |\n| Abstract Reasoning | ARC-AGI-1 | 84.0% |\n| Abstract Reasoning | ARC-AGI-2 | 40.1% |\n| Chat | IFBench | 82.2% |\n| Vision | MMMU Pro (Standard 10) | 74.0% |\n| Audio | MMAU | 77.0% |\n| Audio | VoiceBench | 90.1% |\n\n**Data Collection Method by Dataset:** Hybrid: Automated, Manually-Collected. \n**Labeling Method by Dataset:** Undisclosed \n**Properties:** Evaluation covers coding agents, tool use, general and abstract reasoning, factuality, instruction following, multilingual knowledge, safety, vision understanding, and audio understanding. See the [linked third-party model card](https://huggingface.co/thinkingmachines/Inkling-Small#5-evaluations) for the complete comparison tables, harness notes, and methodology. NVIDIA has not independently reproduced the linked results.\n\n## Inference\n\n**Acceleration Engine:** SGLang and vLLM are supported through public deployment recipes. \n**Test Hardware:** NNVIDIA Blackwell (GB200, B200, B300, GB300), NVIDIA Hopper (H200)\n\n## Known Limitations\n\nInkling-Small may exhibit limitations common to foundation models, including hallucinations, occasional failures to follow instructions precisely, and degraded performance in long, multi-turn conversations. The model may reflect demographic, cultural, or linguistic biases in its web-derived and synthetic training data and may perform unevenly across languages, dialects, or subject areas that were less represented during training.\n\nThe model's knowledge is limited to information available as of its training cutoff, which is not specified in the source model card. It may not reflect later events or developments.\n\nDownstream developers should:\n\n- Evaluate performance, safety, and fairness for their specific use case, language, and population before deployment.\n- Apply content filtering, rate limiting, monitoring, and other application-layer safeguards.\n- Use human review for high-stakes or safety-critical outputs.\n- Avoid medical, legal, or other safety-critical decision-making deployments without additional fine-tuning, domain-specific validation, and human oversight.\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 and video content; if image or video includes people, personal health information, or intellectual property, the image or video 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### Enterprise Support\n\nFor help with NVIDIA NIM, customers with an active NVIDIA AI Enterprise subscription can [file a support ticket](https://www.nvidia.com/en-us/support/enterprise/).\n\n### NVIDIA Developer Community Forum\n\nAccess community knowledge-base articles and support cases through the [NVIDIA Developer Forums](https://forums.developer.nvidia.com/).","displayName","Inkling Small",["D",1793770454492],"publisher","Thinking Machines Lab","latestVersion","collectionCounts","imageUrl","/api/image/nim/thinkingmachines/models/inkling-small","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[265,266],"displayLabels",[254,255],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_223":263,"_257":263,"_258":264,"_260":261},{"_223":256,"_257":256,"_258":259,"_260":261},"NSPECT-CYSD-LTCW","value","filter",{"_262":261,"_257":256},"category","label","field","False",{"_262":261,"_257":263},{"_223":272,"_257":272,"_258":273,"_260":270},{"_223":267,"_257":268,"_258":269,"_260":270},"NVIDIA NIM","nimmcro_nvidia_nim",{"_262":271,"_257":268},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_262":261,"_257":272},{"_127":1408,"_129":1409},"ngcApps",[1361],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1342":1343,"_1344":1345,"_1346":1347,"_1348":1349,"_1350":285,"_158":311,"_1351":1352,"_1353":59,"_1354":90,"_1355":90,"_1356":1357,"_1358":90,"_1359":1360},"amplitude",{"_1339":1340,"_946":-7,"_1341":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1333":1334,"_1335":1336,"_1337":1338},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1793926942414,"1.30.0","defaultSearchSuggestions",{"_1216":192,"_1217":1218,"_1219":1220,"_1221":1222},"announcements",[],"consentState",{"_349":1206,"_1207":35,"_1208":35,"_1209":90,"_1210":1211},"consentConfig",{"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350},"showContactAdmin",1793926942415,[],[],[325,326,327,328,329],{"_49":63,"_51":73,"_53":122,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":70,"_53":122,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":64,"_53":122,"_55":-5,"_57":-5,"_58":59},{"_49":60,"_51":61,"_53":122,"_55":-5,"_57":-5,"_58":59},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":330,"_58":59},4,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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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.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","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. 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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.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","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.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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