Granite 4.1 LLMs: How They’re Built

An in-depth technical walkthrough of data engineering, pre-training, supervised fine-tuning, and reinforcement learning behind the Granite 4.1 LLMs.

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). Notably, the 8B instruct model matches or surpasses the previous Granite 4.0‑H‑Small (32B‑A9B MoE) despite using a simpler dense architecture with fewer parameters. All Granite 4.1 models are released under the Apache 2.0 license.

Links:

Granite 4.1 HF Collection
GitHub Repository
Granite Docs

Overview

Building high‑quality small language models goes beyond simply scaling compute—it requires rigorous data curation throughout training. For Granite 4.1, we prioritized data quality over quantity, progressively refining the data mixture across five pre‑training stages. We further curated supervised fine‑tuning data using an LLM‑as‑Judge framework and applied a multi‑stage reinforcement learning pipeline to systematically strengthen performance in math, coding, instruction following, and general chat.

Model Architecture

Granite 4.1 models use a decoder-only dense transformer architecture. The core design choices include Grouped Query Attention (GQA), Rotary Position Embeddings (RoPE), SwiGLU activations, RMSNorm, and shared input/output embeddings.

Component	3B Dense	8B Dense	30B Dense
Embedding size	2560	4096	4096
Number of layers	40	40	64
Attention head size	64	128	128
Number of attention heads	40	32	32
Number of KV heads	8	8	8
MLP hidden size	8192	12800	32768
MLP activation	SwiGLU	SwiGLU	SwiGLU
Position embedding	RoPE	RoPE	RoPE