---
title: "IBM Granite (language model)"
source: "https://systems-analysis.info/eng/IBM_Granite_(language_model)"
wiki: "systems-analysis.info/eng"
article: "IBM_Granite_(language_model)"
language: "en"
categories:
  - "Category:English"
  - "Category:Large language models"
  - "Category:LLM families"
  - "Category:Machine learning"
  - "Category:Technology"
revision_id: 188
wiki_created_at: 2026-09-06T22:18:39Z
wiki_modified_at: 2026-09-06T22:18:39Z
downloaded_at: 2026-09-07T22:21:42Z
---

# IBM Granite (language model)

**IBM Granite** is a series of [large language models](https://systems-analysis.info/eng/Large_language_model "Large language model") (LLMs) developed by IBM for enterprise applications. Most Granite models are autoregressive, [decoder-only transformers](https://systems-analysis.info/eng/Decoder-only_models_(architecture) "Decoder-only models (architecture)") capable of generating text based on a given context, though the latest **Granite 4.0** generation (2025) adopts a hybrid Mamba-2/Transformer architecture<sup>[\[1\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-ibm_blog_building-1)[\[2\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite4_announcement-2)</sup>.

The family was officially introduced on September 7, 2023, as part of the launch of the IBM watsonx.ai cloud platform<sup>[\[3\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-techcrunch_2023-3)</sup>. IBM positions Granite as open, high-performance, and reliable solutions for enterprises, emphasizing training data transparency, risk management, and licensing that permits commercial use<sup>[\[4\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite_main_page-4)</sup>.

## Development History

The Granite family of models became part of IBM's strategy to provide businesses with its own generative models alongside models from its partners.

- **September 2023**: Official announcement and launch of the first models on the watsonx.ai platform. The first releases, **Granite.13b.instruct** and **Granite.13b.chat**, had approximately 13 billion parameters (Granite.13b.v1 was trained on about 1 trillion tokens from 14 datasets) and were focused on key language processing tasks<sup>[\[3\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-techcrunch_2023-3)[\[5\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-redhat_training_data-5)</sup>.
- **May 2024**: IBM releases several **Granite Code** models (3, 8, 20, and 34 billion parameters) as open source under the Apache 2.0 license. The model weights were published on the [Hugging Face](https://systems-analysis.info/eng/Hugging_Face "Hugging Face") platform, marking a significant step in supporting the open AI ecosystem<sup>[\[6\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-ibm_code_open-6)</sup>.
- **October 2024**: IBM releases **Granite 3.0**, the third generation, comprising dense models (2 and 8 billion parameters), sparse Mixture-of-Experts models (1B-A400M and 3B-A800M, i.e. 1 and 3 billion total parameters with 400M and 800M active), and Granite Guardian 3.0 safety models. All are released under Apache 2.0 and trained on over 12 trillion tokens<sup>[\[7\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite3_announcement-7)</sup>.
- **December 2024**: **Granite 3.1** extends the context window to 128,000 tokens and adds new embedding models<sup>[\[8\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite31_announcement-8)</sup>.
- **February 2025**: **Granite 3.2** introduces optional, programmatically toggleable chain-of-thought reasoning within the standard models, along with a multimodal **Granite Vision 3.2** (2B) model focused on document understanding<sup>[\[9\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite32_siliconangle-9)</sup>.
- **2025**: **Granite 3.3** updates the 8B and 2B language models and adds Granite Speech models for speech recognition and translation<sup>[\[10\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite3_huggingface-10)</sup>.
- **October 2025**: **Granite 4.0** marks the family's first shift away from a pure transformer design, introducing a hybrid Mamba-2/Transformer architecture. The release includes Granite-4.0-Micro (3B dense), Granite-4.0-H-Micro (3B hybrid), Granite-4.0-H-Tiny (7B total, ~1B active), and Granite-4.0-H-Small (32B total, ~9B active), all under Apache 2.0<sup>[\[2\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite4_announcement-2)</sup>. Later the same month IBM added the ultra-compact **Granite 4.0 Nano** models (about 350M and 1B parameters, in both hybrid and transformer variants) for on-device and in-browser inference<sup>[\[11\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite4_nano-11)</sup>.

## Architecture and Training

### Architecture

The original **Granite.13b** model is built on a [transformer](https://systems-analysis.info/eng/Transformer_architecture "Transformer architecture") decoder (*decoder-only*) architecture, similar to the [GPT](https://systems-analysis.info/eng/GPT_(OpenAI) "GPT (OpenAI)") models, using the **multi-query attention** mechanism with a [context window](https://systems-analysis.info/eng/Context_window "Context window") of up to 8,000 [tokens](https://systems-analysis.info/eng/Token_(LLM) "Token (LLM)")<sup>[\[12\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite_tech_doc-12)</sup>. The architecture has since evolved: the **Granite 3.x** models use grouped-query attention (GQA), rotary position embeddings (RoPE), SwiGLU activations and RMSNorm, and extend the context window to 128,000 tokens<sup>[\[13\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-nvidia_granite3-13)</sup>.

The **Granite 4.0** generation departs from this pattern, interleaving a small number of standard transformer attention blocks with a majority of Mamba-2 state-space layers in a 9:1 ratio and removing positional encodings entirely. The Mamba-2 layers process global context linearly and efficiently, periodically passing information to a transformer block for more nuanced local parsing; the Mixture-of-Experts variants additionally use always-active shared experts. IBM reports that this design cuts the memory required for long inputs and concurrent batches by more than 70% compared with conventional transformers. Granite 4.0 was trained on samples of up to 512,000 tokens and validated up to 128,000 tokens<sup>[\[2\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite4_announcement-2)[\[14\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-infoworld_granite4-14)</sup>. All models are trained using *self-supervised learning*.

### Training Data and AI Governance

A key feature of Granite is the use of a proprietary, curated data corpus selected for enterprise needs. Unlike many LLMs trained on unfiltered web scrapes, Granite was trained on high-quality (*enterprise-quality*) data covering several domains<sup>[\[12\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite_tech_doc-12)</sup>: academic and scientific literature, software code in numerous languages, legal texts (court decisions, public reports), corporate financial statements, and filtered general-purpose internet text. The corpus has grown across generations, reaching over 12 trillion tokens for Granite 3.0 and about 22 trillion tokens for Granite 4.0<sup>[\[7\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite3_announcement-7)[\[14\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-infoworld_granite4-14)</sup>.

IBM emphasizes that the development followed strict principles of **AI Governance** (ethics and data management). Each data segment underwent a verification process to ensure compliance with corporate policies. To remove undesirable content, an internal detector called **"HAP"** (Hate, Abuse, and Profanity) and automated blocklists of web resources were used<sup>[\[1\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-ibm_blog_building-1)[\[15\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-ibm_hap-15)</sup>. IBM published a detailed technical report with a list of its sources, a rare step for major technology companies that ensures a high degree of transparency.

## The Granite Model Family

The IBM Granite family includes several categories of models for various business tasks:

- **Granite Language Models**: Base and instruction-tuned models for text processing tasks such as generation, summarization, classification, etc.
- **Granite Code Models**: Specialized LLMs trained on over 100 programming languages for code completion, generation, and fixing. Available in sizes ranging from 3 to 34 billion parameters<sup>[\[6\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-ibm_code_open-6)</sup>.
- **Granite Vision Models**: Neural networks for image and document analysis, text recognition, and content understanding.
- **Granite Speech Models**: Compact models for speech recognition and translation.
- **Granite for Time Series**: Specialized models for time series forecasting.
- **Granite for Geospatial**: Developed in collaboration with NASA for analyzing satellite imagery and other geospatial data.
- **Granite [Embedding](https://systems-analysis.info/eng/Embedding_(NLP) "Embedding (NLP)") Models**: Models for semantic search tasks and building [RAG](https://systems-analysis.info/eng/Retrieval-augmented_generation_(RAG) "Retrieval-augmented generation (RAG)") systems.
- **Granite Guardian**: A specialized family of safety models designed to filter undesirable queries and monitor content, including detection of hate, abuse and profanity, jailbreak attempts, and hallucinations in RAG and tool-use workflows.

## Open Source and Licensing

IBM has placed a significant emphasis on the openness of the Granite family, positioning it as a transparent alternative to closed, proprietary LLMs. In May 2024, the company released the base Granite Code models to the open-source community under the **Apache 2.0** license, which allows them to be freely used, modified, and distributed; subsequent generations, including Granite 3.0 and Granite 4.0, have also been released under Apache 2.0<sup>[\[6\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-ibm_code_open-6)[\[7\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite3_announcement-7)</sup>.

This approach, along with the publication of detailed information about its training data, earned IBM high praise from the research community. In Stanford University's **Foundation Model Transparency Index** (FMTI), Granite scored a perfect 100% in several transparency categories in 2024, and in the 2025 edition IBM Granite 3.3 was ranked the most transparent of the 13 evaluated models, scoring 95% — the highest mark in the index's three-year history and 23 points ahead of the runner-up<sup>[\[4\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite_main_page-4)[\[16\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-ibm_fmti_2025-16)</sup>. With Granite 4.0, IBM also stated that the family is the first set of open models to be covered by an accredited ISO/IEC 42001 certification for its AI management system, and that all Granite 4.0 checkpoints are cryptographically signed<sup>[\[2\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite4_announcement-2)</sup>.

## Applications

The Granite models are integrated into the IBM watsonx cloud platform and are used in various enterprise scenarios.

- **Sports Analytics (US Open)**: In collaboration with the United States Tennis Association (USTA), IBM uses Granite to automatically generate match reports and audio commentary for each match at the US Open tournament. The solution generates a detailed text summary of the match within minutes of its conclusion<sup>[\[17\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-usopen_2024-17)</sup>.
- **Developer Assistance**: The Granite Code models form the basis of **IBM watsonx Code Assistant**, a family of tools that can, for example, automatically convert legacy COBOL code into modern microservices for IBM Z<sup>[\[6\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-ibm_code_open-6)</sup>.
- **Industry-Specific AI Applications**: Lockheed Martin has integrated Granite models into its AI Factory platform for national security tasks. ESPN uses Granite to generate personalized commentary for fantasy sports<sup>[\[4\]](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_note-granite_main_page-4)</sup>.

## External links

- <a href="https://www.ibm.com/granite" class="external text" rel="nofollow">Official IBM Granite page</a>
- <a href="https://huggingface.co/ibm-granite" class="external text" rel="nofollow">IBM Granite on Hugging Face</a>
- <a href="https://en.wikipedia.org/wiki/IBM_Granite" class="external text" rel="nofollow">IBM Granite — Wikipedia</a>

## Literature

- Ainslie, J. et al. (2023). *GQA: Training Generalized Multi‑Query Transformer Models from Multi‑Head Checkpoints*. <a href="https://arxiv.org/abs/2305.13245" class="external text" rel="nofollow">arXiv:2305.13245</a>.
- Awasthy, P. et al. (2025). *Granite Embedding Models*. <a href="https://arxiv.org/abs/2502.20204" class="external text" rel="nofollow">arXiv:2502.20204</a>.
- Dao, T. et al. (2022). *FlashAttention: Fast and Memory‑Efficient Exact Attention with IO‑Awareness*. <a href="https://arxiv.org/abs/2205.14135" class="external text" rel="nofollow">arXiv:2205.14135</a>.
- Gu, A.; Dao, T. (2023). *Mamba: Linear‑Time Sequence Modeling with Selective State Spaces*. <a href="https://arxiv.org/abs/2312.00752" class="external text" rel="nofollow">arXiv:2312.00752</a>.
- Fedus, W.; Zoph, B.; Shazeer, N. (2021). *Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity*. <a href="https://arxiv.org/abs/2101.03961" class="external text" rel="nofollow">arXiv:2101.03961</a>.
- Granite Vision Team (2025). *Granite Vision: A Lightweight, Open‑Source Multimodal Model for Enterprise Intelligence*. <a href="https://arxiv.org/abs/2502.09927" class="external text" rel="nofollow">arXiv:2502.09927</a>.
- Granite Team, IBM (2024). *Granite 3.0 Language Models*. <a href="https://github.com/ibm-granite/granite-3.0-language-models/" class="external text" rel="nofollow">Technical report</a>.
- Mishra, M. et al. (2024). *Granite Code Models: A Family of Open Foundation Models for Code Intelligence*. <a href="https://arxiv.org/abs/2405.04324" class="external text" rel="nofollow">arXiv:2405.04324</a>.
- Padhi, I. et al. (2024). *Granite Guardian: Risk Detection for Safe and Responsible Use of LLMs*. <a href="https://arxiv.org/abs/2412.07724" class="external text" rel="nofollow">arXiv:2412.07724</a>.
- Peng, B. et al. (2023). *YaRN: Efficient Context Window Extension of Large Language Models*. <a href="https://arxiv.org/abs/2309.00071" class="external text" rel="nofollow">arXiv:2309.00071</a>.
- Stallone, M. et al. (2024). *Scaling Granite Code Models to 128K Context*. <a href="https://arxiv.org/abs/2407.13739" class="external text" rel="nofollow">arXiv:2407.13739</a>.

## References

1.  <span id="cite_note-ibm_blog_building-1">↑ <sup>[1.0](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-ibm_blog_building_1-0)</sup> <sup>[1.1](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-ibm_blog_building_1-1)</sup> "Building AI for business: IBM's Granite foundation models". *IBM*. <a href="https://www.ibm.com/think/news/granite-foundation-models" class="external autonumber" rel="nofollow">[1]</a></span>
2.  <span id="cite_note-granite4_announcement-2">↑ <sup>[2.0](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite4_announcement_2-0)</sup> <sup>[2.1](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite4_announcement_2-1)</sup> <sup>[2.2](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite4_announcement_2-2)</sup> <sup>[2.3](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite4_announcement_2-3)</sup> "IBM Granite 4.0: Hyper-efficient, High Performance Hybrid Models for Enterprise". *IBM*. <a href="https://www.ibm.com/new/announcements/ibm-granite-4-0-hyper-efficient-high-performance-hybrid-models" class="external autonumber" rel="nofollow">[2]</a></span>
3.  <span id="cite_note-techcrunch_2023-3">↑ <sup>[3.0](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-techcrunch_2023_3-0)</sup> <sup>[3.1](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-techcrunch_2023_3-1)</sup> Lardinois, Frederic (September 7, 2023). "IBM rolls out new generative AI features and models". *TechCrunch*. <a href="https://techcrunch.com/2023/09/07/ibm-rolls-out-new-generative-ai-features-and-models/" class="external autonumber" rel="nofollow">[3]</a></span>
4.  <span id="cite_note-granite_main_page-4">↑ <sup>[4.0](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite_main_page_4-0)</sup> <sup>[4.1](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite_main_page_4-1)</sup> <sup>[4.2](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite_main_page_4-2)</sup> "Granite". *IBM*. <a href="https://www.ibm.com/granite" class="external autonumber" rel="nofollow">[4]</a></span>
5.  <span id="cite_note-redhat_training_data-5">[↑](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-redhat_training_data_5-0) "IBM's Granite foundation model: A detailed look at its training data". *Red Hat Blog*. <a href="https://www.redhat.com/en/blog/ibms-granite-foundation-model-detailed-look-its-training-data" class="external autonumber" rel="nofollow">[5]</a></span>
6.  <span id="cite_note-ibm_code_open-6">↑ <sup>[6.0](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-ibm_code_open_6-0)</sup> <sup>[6.1](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-ibm_code_open_6-1)</sup> <sup>[6.2](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-ibm_code_open_6-2)</sup> <sup>[6.3](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-ibm_code_open_6-3)</sup> "IBM's Granite code model family is going open source". *IBM Research Blog*. <a href="https://research.ibm.com/blog/granite-code-models-open-source" class="external autonumber" rel="nofollow">[6]</a></span>
7.  <span id="cite_note-granite3_announcement-7">↑ <sup>[7.0](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite3_announcement_7-0)</sup> <sup>[7.1](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite3_announcement_7-1)</sup> <sup>[7.2](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite3_announcement_7-2)</sup> "IBM Introduces Granite 3.0: High Performing AI Models Built for Business". *IBM Newsroom*. <a href="https://newsroom.ibm.com/2024-10-21-ibm-introduces-granite-3-0-high-performing-ai-models-built-for-business" class="external autonumber" rel="nofollow">[7]</a></span>
8.  <span id="cite_note-granite31_announcement-8">[↑](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite31_announcement_8-0) "IBM Granite 3.1: powerful performance, longer context and more". *IBM*. <a href="https://www.ibm.com/new/announcements/ibm-granite-3-1-powerful-performance-long-context-and-more" class="external autonumber" rel="nofollow">[8]</a></span>
9.  <span id="cite_note-granite32_siliconangle-9">[↑](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite32_siliconangle_9-0) "IBM debuts new Granite 3.2 family of models that include reasoning when you want it". *SiliconANGLE*. <a href="https://siliconangle.com/2025/02/26/ibm-releases-new-granite-3-2-family-models-include-reasoning-want/" class="external autonumber" rel="nofollow">[9]</a></span>
10. <span id="cite_note-granite3_huggingface-10">[↑](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite3_huggingface_10-0) "Granite 3.3 Language Models - a ibm-granite Collection". *Hugging Face*. <a href="https://huggingface.co/collections/ibm-granite/granite-33-language-models-67f65d0cca24bcbd1d3a08e3" class="external autonumber" rel="nofollow">[10]</a></span>
11. <span id="cite_note-granite4_nano-11">[↑](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite4_nano_11-0) "IBM's open source Granite 4.0 Nano AI models are small enough to run locally directly in your browser". *VentureBeat*. <a href="https://venturebeat.com/ai/ibms-open-source-granite-4-0-nano-ai-models-are-small-enough-to-run-locally" class="external autonumber" rel="nofollow">[11]</a></span>
12. <span id="cite_note-granite_tech_doc-12">↑ <sup>[12.0](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite_tech_doc_12-0)</sup> <sup>[12.1](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-granite_tech_doc_12-1)</sup> "Granite Foundation Models: Technical Specifications". *IBM*. <a href="https://www.ibm.com/downloads/cas/X9W4O6BM" class="external autonumber" rel="nofollow">[12]</a></span>
13. <span id="cite_note-nvidia_granite3-13">[↑](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-nvidia_granite3_13-0) "IBM's New Granite 3.0 Generative AI Models Are Small, Yet Highly Accurate and Efficient". *NVIDIA Technical Blog*. <a href="https://developer.nvidia.com/blog/ibms-new-granite-3-0-generative-ai-models-are-small-yet-highly-accurate-and-efficient/" class="external autonumber" rel="nofollow">[13]</a></span>
14. <span id="cite_note-infoworld_granite4-14">↑ <sup>[14.0](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-infoworld_granite4_14-0)</sup> <sup>[14.1](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-infoworld_granite4_14-1)</sup> "IBM launches Granite 4.0 to cut AI infra costs with hybrid Mamba-transformer models". *InfoWorld*. <a href="https://www.infoworld.com/article/4067691/ibm-launches-granite-4-0-to-cut-ai-infra-costs-with-hybrid-mamba-transformer-models.html" class="external autonumber" rel="nofollow">[14]</a></span>
15. <span id="cite_note-ibm_hap-15">[↑](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-ibm_hap_15-0) "IBM open sources fast HAP filter on Hugging Face". *IBM Research*. <a href="https://research.ibm.com/blog/efficient-llm-hap-detector" class="external autonumber" rel="nofollow">[15]</a></span>
16. <span id="cite_note-ibm_fmti_2025-16">[↑](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-ibm_fmti_2025_16-0) "IBM recognized as the leader in AI transparency". *IBM*. <a href="https://www.ibm.com/new/announcements/ibm-leads-the-industry-in-ai-transparency" class="external autonumber" rel="nofollow">[16]</a></span>
17. <span id="cite_note-usopen_2024-17">[↑](https://systems-analysis.info/eng/IBM_Granite_(language_model)#cite_ref-usopen_2024_17-0) "IBM and the USTA Serve Up New and Enhanced Generative AI Features for 2024 US Open Digital Platforms". *IBM Newsroom*. <a href="https://newsroom.ibm.com/2024-08-15-ibm-and-the-usta-serve-up-new-and-enhanced-generative-ai-features-for-2024-us-open-digital-platforms" class="external autonumber" rel="nofollow">[17]</a></span>