# Granite 4.1 LLMs: How They're Built

DevFeed: [Granite 4.1 LLMs: How They're Built](<https://devfeed.tech/articles/granite-4-1-llms-how-they-re-built-7256.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/ibm-granite/granite-4-1>)

Author: Yousaf Shah

Published: 2026-04-29T15:01:48Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [LLMs](<https://devfeed.tech/topics/llms.md>), [ibm](<https://devfeed.tech/topics/ibm.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [grpo](<https://devfeed.tech/topics/grpo.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [Transformer architecture](<https://devfeed.tech/topics/transformer-architecture.md>), [Data Quality](<https://devfeed.tech/topics/data-quality.md>)

Tags: [apache](<https://devfeed.tech/tags/apache.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [grpo](<https://devfeed.tech/tags/grpo.md>), [ibm](<https://devfeed.tech/tags/ibm.md>), [learning](<https://devfeed.tech/tags/learning.md>), [llms](<https://devfeed.tech/tags/llms.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training](<https://devfeed.tech/tags/training.md>), [transformer-architecture](<https://devfeed.tech/tags/transformer-architecture.md>)

## AI overview

The article introduces Granite 4.1, IBM's family of dense decoder-only LLMs in 3B, 8B, and 30B sizes. It describes their five-stage training process, which uses about 15 trillion tokens, data-quality refinement, long-context extension up to 512K tokens, supervised fine-tuning, and reinforcement learning with on-policy GRPO and DAPO loss. The models use a dense transformer architecture and are released under the Apache 2.0 license.

## Source excerpt

Authors: Granite Team, IBM TL;DR -- Granite 4.1 is a family of dense, decoder-only LLMs (3B, 8B, and 30B) trained on ~15T tokens using a multi-stage pre-training pipeline, including long-context extension of up to 512K tokens. The models are further refined with supervised fine-tuning on ~4.1M high-quality curated samples and reinforcement learning via on-policy GRPO with DAPO loss (Yu et al., 2025).