RedHatAI/granite-3.3-8b-instruct

TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:Sep 16, 2025License:apache-2.0Architecture:Transformer0.0K Open Weights Cold

Granite-3.3-8B-Instruct is an 8-billion parameter language model developed by the Granite Team at IBM, featuring a 32K context length. Fine-tuned for enhanced reasoning and instruction-following, it supports structured thinking with and tags. This model demonstrates significant improvements in mathematics, coding, and general instruction-following benchmarks, making it suitable for AI assistants and business applications.

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Granite-3.3-8B-Instruct: Enhanced Reasoning and Instruction Following

Granite-3.3-8B-Instruct is an 8-billion parameter language model from the Granite Team at IBM, released on April 16th, 2025. Built upon Granite-3.3-8B-Base, this model is fine-tuned for improved reasoning and instruction-following, featuring a 32K context length. A key differentiator is its support for structured reasoning using <think> and <response> tags, which clearly separate internal thought processes from final outputs.

Key Capabilities

  • Enhanced Reasoning: Demonstrates significant gains on benchmarks like AlpacaEval-2.0 and Arena-Hard, with notable improvements in mathematics and coding tasks.
  • Instruction Following: Excels in general instruction-following, making it suitable for AI assistants and business applications.
  • Multilingual Support: Supports English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese.
  • Long-Context Tasks: Capable of handling long document summarization and question-answering.
  • Code-Related Tasks: Proficient in code generation and related tasks.

Performance Highlights

Granite-3.3-8B-Instruct shows strong performance across various benchmarks, particularly in math, with an 8.12 score on AIME24 and 69.02 on MATH-500. It also achieves 89.73 on HumanEval and 86.09 on HumanEval+, indicating robust coding capabilities. The model was trained on a balanced combination of permissively licensed data and curated synthetic tasks using IBM's Blue Vela supercomputing cluster.

Good for

  • Developing AI assistants requiring strong instruction-following.
  • Applications needing structured reasoning and clear output separation.
  • Tasks involving mathematics, coding, and long-context understanding.
  • Multilingual applications across the 12 supported languages.