granite-3.1-8b-instruct
Granite-3.1-8B-Instruct is an 8-billion parameter instruction-tuned language model developed by IBM. It is designed for general-purpose AI assistants and supports 12 languages. The model is released under Apache 2.0 license, making it freely usable for both open-source and commercial projects. It is not gated, meaning anyone can download and use it immediately.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | ibm-granite |
| Parameters | 8.2B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 132k |
| Likes | 168 |
| Last updated | 2025-04-16 |
| Source | ibm-granite/granite-3.1-8b-instruct |
What granite-3.1-8b-instruct is
An instruction-finetuned decoder-only transformer with 8.17B parameters, built on Granite-3.1-8B-Base. Training combined permissively-licensed open-source instruction datasets with IBM's synthetic long-context datasets. Finetuning employed supervised instruction tuning, reinforcement learning-based alignment, and model merging. Context length is not specified in the card. Evaluated on HuggingFace Open LLM Leaderboard V1 (avg 71.31) and V2 (avg 30.55).
Run granite-3.1-8b-instruct locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="ibm-granite/granite-3.1-8b-instruct")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE — fp32 inference: ~16–20 GB VRAM (single GPU). fp16 inference: ~8–10 GB VRAM. Quantized (int8/int4): potentially 2–4 GB. Verify with your deployment target (VRAM, precision, batch size). Not specified in card whether bfloat16 is supported.
Model is a finetuned variant of Granite-3.1-8B-Base using instruction datasets and synthetic long-context data. No explicit LoRA/QLoRA guidance in the card. Finetuning feasibility depends on your hardware and target task; LoRA is standard for 8B models on consumer/mid-range GPUs. Recommend review of Granite GitHub repo for finetuning recipes.
When to avoid it — and what to weigh
- Extreme Low-Latency or Resource-Constrained Environments — 8B parameters require ~16–20 GB VRAM for inference in fp32 (or ~8–10 GB in fp16). If you need sub-millisecond latency or sub-2GB device memory, a smaller model (2B or 1B variant) may be better, or evaluate quantized versions.
- Advanced Reasoning or Math Tasks — HuggingFace Open LLM Leaderboard V2 shows modest scores on MATH Level 5 (21.68) and GPQA (8.28). Not recommended for high-stakes math problem-solving or research-grade reasoning.
- Expert Fact-Checking or High-Precision Knowledge Tasks — TruthfulQA score of 66.23 indicates room for improvement in factual accuracy. Not suitable for applications where hallucination or false claims pose significant risk (e.g., medical/legal advisories without guardrails).
- Languages Beyond the 12 Supported — Only 12 languages are officially supported. Model card states users may finetune for other languages, but out-of-the-box performance on unsupported languages is unknown.
License & commercial use
Apache License 2.0 (OSI-approved, permissive open-source license). Full rights to use, modify, and distribute. No commercial restrictions.
Commercial use is permitted under Apache 2.0. You may build products, services, and proprietary applications on top of this model without license fees or IBM approval. However: (1) retain Apache 2.0 license notices in derivative works, (2) perform your own testing for production safety/compliance (data privacy, bias, hallucination risk), and (3) IBM provides no warranty or liability indemnification. Verify your use case does not implicate regulated domains (healthcare, finance) where model performance may be insufficient.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security audit, adversarial robustness testing, or vulnerability disclosure process mentioned. Standard LLM security concerns apply: (1) model can generate plausible but false outputs (hallucinations), (2) susceptible to prompt injection if not sandboxed, (3) may reflect training data biases or memorized sensitive text (review your data governance policy before production use), (4) no explicit data retention or model deletion guarantees. Recommend red-teaming and input validation in production deployments.
Alternatives to consider
Mistral-7B or Mixtral-8x7B
Similar parameter count, permissive license (MIT or Apache 2.0). Comparable or stronger benchmark performance. Active community support and multiple serving frameworks.
Llama 2 13B or 70B
Larger parameter options; stronger reasoning. License is non-commercial without commercial agreement. Requires Llama Community License review for production.
Granite-3.1-2B-Instruct (smaller variant)
Same family, half the parameters, lower VRAM footprint (~4–5 GB fp16). Acceptable for edge/latency-sensitive deployments if benchmark scores are sufficient for your use case.
Ship granite-3.1-8b-instruct with senior software developers
Granite-3.1-8B-Instruct is permissively licensed and production-ready. Explore private hosting options, RAG integration, or custom finetuning with Devco.
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granite-3.1-8b-instruct FAQ
Can I use Granite-3.1-8B-Instruct commercially?
What GPU do I need to run this model?
Can I finetune Granite-3.1-8B-Instruct for my own domain?
What languages does this model support?
Software development & web development with DEV.co
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Ready to Deploy an Open-Source LLM?
Granite-3.1-8B-Instruct is permissively licensed and production-ready. Explore private hosting options, RAG integration, or custom finetuning with Devco.