granite-3.3-8b-instruct
Granite-3.3-8B-Instruct is an 8-billion parameter instruction-tuned language model from IBM released in April 2025. It supports 128K context length and is optimized for reasoning, coding, and instruction-following tasks. The model uses structured thinking tags to separate internal reasoning from final outputs. It is permissively licensed under Apache 2.0, ungated, and available via HuggingFace with ~97k downloads.
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 | 97k |
| Likes | 158 |
| Last updated | 2025-05-12 |
| Source | ibm-granite/granite-3.3-8b-instruct |
What granite-3.3-8b-instruct is
A transformer-based causal language model (8.17B parameters) fine-tuned on permissively licensed data and synthetic tasks. Supports chat templates with optional thinking mode (<think>/<response> tags). Trained for 12 languages (English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, Chinese). Context length: 128K. Serving framework: transformers + accelerate. Precision: bfloat16 recommended.
Run granite-3.3-8b-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-3.3-8b-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: ~16–32 GB VRAM for bfloat16 full inference (unquantized). ~8–12 GB with 8-bit quantization (GPTQ/AWQ). Batch serving (vLLM/TGI) recommended to amortize memory overhead. CPU-only inference via llama.cpp feasible for latency-tolerant applications but slow. Verify against your target hardware before production.
LoRA/QLoRA fine-tuning is feasible; model card does not restrict it. Apache 2.0 permits derivative works. Synthetic task composition and instruction-tuning suggest the base model is receptive to task-specific adaptation. No guidance on optimal LoRA rank, learning rates, or data requirements provided; requires empirical tuning.
When to avoid it — and what to weigh
- Extreme Low-Latency Requirements — 8B parameters, while moderate, require GPU memory and inference time suitable for batch/async workflows. Real-time sub-100ms SLA endpoints may need quantization or distillation.
- Proprietary/Closed Data Training — Model trained on permissively licensed data. If your use case requires fine-tuning on proprietary data with restricted derivative-work licensing, review Apache 2.0 implications with legal counsel.
- Safety-Critical or High-Assurance Domains — No safety audit, robustness testing, or adversarial attack mitigation data provided. Not recommended for medical diagnosis, autonomous driving, or regulated security systems without independent validation.
- Unsupported Language Use — While fine-tuning for additional languages is possible, model has no native training data for languages outside the 12 listed. Performance on unlisted languages is Unknown.
License & commercial use
Apache License 2.0. Permissive, OSI-approved license permitting commercial use, modification, and distribution provided attribution and license text are included. No restrictions on derivative works or closed-source deployment.
Commercial use is explicitly permitted under Apache 2.0. You may build proprietary products, offer SaaS, or redistribute provided you include the license notice and source attribution. No royalties or special agreements required. Verify your derivative licensing does not conflict with Apache 2.0 clause 4 (litigation/patent indemnity) in your legal review.
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 security audit, vulnerability disclosure, or adversarial robustness testing is documented. Model trained on permissively licensed data; supply chain integrity depends on HuggingFace repository integrity and IBM's training infrastructure. Standard precautions: validate model provenance, run isolated testing, apply content filters for production, and monitor for prompt injection and data exfiltration. No specific known vulnerabilities or exploits are disclosed in the data provided.
Alternatives to consider
Meta Llama 3.1 8B Instruct
Comparable 8B instruct model, also permissively licensed (Llama 2 Community License — requires review for commercial terms). Larger community adoption, more benchmarks. Context length similar or less; choose based on benchmark alignment.
Mistral 7B Instruct v0.3
7B alternative, Apache 2.0 licensed, lower compute footprint. Trade-off: slightly smaller model vs. lower latency. No 128K context; better for < 32K token tasks.
Qwen2 8B Instruct
8B, Apache 2.0 licensed, strong multilingual and coding performance. No confirmed 128K context; verify against your use case.
Ship granite-3.3-8b-instruct with senior software developers
Explore how to integrate this model into your RAG pipeline, custom LLM app, or self-hosted infrastructure. Devco supports end-to-end deployment, fine-tuning, and optimization. Contact our AI engineering team for a technical assessment.
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granite-3.3-8b-instruct FAQ
Can I use Granite-3.3-8B-Instruct in a commercial product?
What GPU do I need to run this model?
Does the model support languages outside the 12 listed?
What is the 128K context length useful for?
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Explore how to integrate this model into your RAG pipeline, custom LLM app, or self-hosted infrastructure. Devco supports end-to-end deployment, fine-tuning, and optimization. Contact our AI engineering team for a technical assessment.