Apple Machine Learning Research
8 models across 4 families · Latest: DCLM 7B (2024-07)
On-device AI prioritizes user privacy
Apple Machine Learning Research's portfolio covers 8 active models across 4 current families, spanning general LLM work. Open a model detail page to compare provider routes and sourced benchmarks.
Covers 0 workload areas across 8 active tracked models; last verified 2026-05-19.
Use it for
- Teams evaluating general LLM work across this lab's releases
- Comparing model families before committing to a flagship
- Migration and pricing follow-ups across 8 tracked models
Do not use it for
- Choosing a hosting provider without opening a model page for price ladders
Active models
8
Current models from this lab, excluding deprecated ones
Active families
4
Current model families from this lab
Open catalog
2 open
2 open source / 0 open weights
Lowest output price
Not tracked
No provider output pricing linked yet
Latest dated release
2024-07-15
DCLM 7B
Freshness
2026-05-19
Researched 60d ago
Information
Release cadence
Showing 5 recent dated releases (full timeline below). Latest: DCLM 7B (2024-07-15).
Where this lab wins
Not enough capability or benchmark coverage yet to call strengths for this lab.
Flagship quality / price signal
Flagship: OpenELM 270M (best sourced coding quality-per-dollar in this portfolio).
Quality-per-dollar unavailable for this flagship — benchmark coverage or output token pricing is still missing.
Apple Machine Learning Research is an American AI research organization founded in 1976. On-device AI prioritizes user privacy. Apple Machine Learning Research ships 4 model families totaling 8 models, with the most recent release DCLM 7B in 2024-07. Notable families include DCLM, Apple Intelligence Server, and Apple Intelligence On-Device. Use it as a stable reference for lab background, release coverage, and follow-up. View official API endpoints, benchmark performance, and coding/agent fit for every Apple Machine Learning Research model.
About
Apple Machine Learning Research is at the forefront of technological advancements in the realm of generative AI and Large Language Models (LLMs). As a division of Apple Inc., which was founded in 1976, this team has been instrumental in driving innovation, with a particular emphasis on integrating AI functionalities directly onto devices to ensure enhanced user privacy and operational efficiency. Apple’s distinct approach to AI development is evident in its commitment to on-device processing, which minimizes reliance on cloud-based systems, thereby safeguarding user data and ensuring quicker response times. A notable aspect of Apple’s research in LLMs is their focus on creating models that are both robust and efficient. These foundation language models have been built to function seamlessly on Apple's range of devices, offering users an array of AI-powered features, such as text generation and refinement, notification summarization, and image creation. These capabilities are spearheaded by Apple Intelligence, an innovative AI system integrated into the latest iOS, iPadOS, and macOS versions. One groundbreaking technique developed by Apple's researchers enables LLMs to run efficiently on devices with limited memory, like iPhones. This is achieved by utilizing flash memory and data optimization methods, which enhance processing speed and reduce memory usage. The benefits of such advancements extend to potential improvements in apps and services like Siri, real-time translation, and augmented reality experiences. Concurrently, their research delves into multimodal AI, where models are trained on both text and images, leading to enhanced performance across AI benchmarks. Apple’s ethical foundation in AI work is underscored by a strong commitment to responsible AI practices. They prioritize user empowerment and privacy, ensuring that user data is not used for training these models. Open-source initiatives, like AXLearn, also demonstrate their dedication to advancing the scalability and efficiency of model training. Further research into privacy-preserving techniques, such as private federated learning and differentially private recommendations, aligns with Apple's ethos of maintaining user trust while delivering cutting-edge AI solutions.
Featured models
| Model | Released | Context | Input price ($/1M) | Output price ($/1M) | License | Openness |
|---|---|---|---|---|---|---|
| DCLM 7B | 2024-07-15 | 2k | - | - | Apache 2.0 | Open source |
| DCLM 7B 8K | 2024-07-15 | 8k | - | - | Apache 2.0 | Open source |
| Apple Server | 2024-06-11 | - | - | - | Proprietary | Proprietary |
Model families
Recent releases
- DCLM 7B- 2024-07-15
- DCLM 7B 8K- 2024-07-15
- Apple Server- 2024-06-11
- Apple On-Device- 2024-06-11
- OpenELM 270M- 2024-04-22
FAQ
Who founded Apple Machine Learning Research and when?
Apple Machine Learning Research was founded in 1976 and is associated with Cupertino, California, United States.
What models has Apple Machine Learning Research released?
Apple Machine Learning Research ships 8 models across 4 families: DCLM, Apple Intelligence Server, and Apple Intelligence On-Device.
Is Apple Machine Learning Research's technology open source?
Some Apple Machine Learning Research models are open-weight (DCLM 7B and DCLM 7B 8K); others are proprietary (Apple Server, Apple On-Device, and OpenELM 270M).
Where is Apple Machine Learning Research headquartered?
Apple Machine Learning Research is headquartered in Cupertino, California, United States.
What is Apple Machine Learning Research known for?
On-device AI prioritizes user privacy. Its most prominent tracked family is DCLM.
How can I access Apple Machine Learning Research's models?
Apple Machine Learning Research's provider availability is tracked on model pages as API and hosting data is verified.
Explore related pages
Last reviewed: 2026-05-19. Data sourced from public lab announcements and provider documentation.