granite-4.1-3b
Granite-4.1-3B is a 3.4B parameter instruction-tuned language model from IBM designed for general-purpose text generation, tool calling, and chat applications. Released April 2026, it supports 12 languages, runs on modest hardware, and is available under Apache 2.0 with no gating. Suitable for business applications, agents, RAG systems, and code tasks.
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 | 3.4B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 321.5k |
| Likes | 86 |
| Last updated | 2026-05-04 |
| Source | ibm-granite/granite-4.1-3b |
What granite-4.1-3b is
Dense transformer model (3.4B parameters) finetuned from Granite-4.1-3B-Base using supervised finetuning and reinforcement learning. Features include tool-calling with OpenAI function schema compatibility, multilingual support, and chat template integration. No context length specified. Trained on permissively-licensed open-source and synthetic data. Compatible with standard HF transformers pipeline; marked for Azure deployment.
Run granite-4.1-3b 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-4.1-3b")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: fp16 inference ~6–8 GB VRAM (single GPU); int8 quantization ~4–6 GB; int4 quantization ~2–3 GB. Training/finetuning estimates require verification. Supports CUDA; CPU inference possible but slow. Multi-GPU inference via transformers device_map.
Model card states users may finetune for languages beyond the 12 supported. LoRA/QLoRA finetuning is standard practice for 3B models on consumer hardware (24 GB+ VRAM recommended for full finetuning). No explicit LoRA checkpoint provided; no guidance on adapter layers, learning rates, or compute cost in excerpt.
When to avoid it — and what to weigh
- Extreme Domain Specialization Without Finetuning — General instruction-tuned model. Specialized medical, legal, or scientific tasks may require domain-specific finetuning or larger models for acceptable accuracy.
- Very Long Context Requirements — Context length is not specified in the model card. If you require extended context (>8k tokens), verify actual context window before deployment.
- Demanding Real-Time Latency SLAs — 3B is smaller and faster than 8B/30B variants, but latency depends on serving infrastructure and batch size. Profile before committing to sub-100ms SLAs.
- Cutting-Edge Reasoning Tasks — MMLU-Pro score (49.83 5-shot CoT) is entry-level. Larger models (8B: 55.99, 30B: 64.09) or specialized reasoning models likely better for complex problem-solving.
License & commercial use
Apache 2.0: permissive OSI license. Allows commercial use, modification, and distribution without royalties or permission requests. Requires attribution and reproduction of license text. No proprietary restrictions.
Apache 2.0 is a permissive OSI license explicitly allowing commercial use. No gating, no closed-weight restrictions. Free to deploy, monetize, and modify in commercial products. No warranty or liability cap beyond standard Apache terms. No commercial support or SLA mentioned in model card; likely available via IBM separately.
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 | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security audit, penetration testing, or adversarial robustness data provided. Model trained on permissively-licensed and synthetic data; no mention of filtering for harmful content or compliance with safety guidelines. Default system prompt (added 2025-10-07) claims to guide toward 'professional, accurate, and safe' responses but no evidence of evaluation. Organizations handling sensitive data should conduct internal red-teaming and validate outputs before production use.
Alternatives to consider
LLaMA 2 / LLaMA 3 (3B, Meta)
Similar size, permissive license, larger community, more finetuning examples; comparable performance but no built-in tool-calling out-of-box.
Mistral 7B (Mistral AI)
Larger (7B), better reasoning (likely higher MMLU-Pro); still runs on moderate hardware; strong commercial backing; no tool-calling optimizations but more capable overall.
Phi-3 (Microsoft, 3.8B–14B variants)
Similar footprint and commercial license; strong instruction-following; less multilingual support but optimized for efficiency; comparable to Granite-4.1-3B in reasoning but with better enterprise backing.
Ship granite-4.1-3b with senior software developers
Download from HuggingFace, review the GitHub repository and Granite Docs, and start with the provided generation and tool-calling examples. For production deployments, assess hardware requirements, finetune for your domain, and conduct security validation. IBM enterprise support may be available separately.
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granite-4.1-3b FAQ
Can I use Granite-4.1-3B in a commercial product?
What GPU do I need to run this model?
Is the context length documented?
Can I finetune Granite-4.1-3B for my domain?
Custom software development services
From first prototype to production, DEV.co delivers software development services around tools like granite-4.1-3b. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source llms and beyond.
Ready to Deploy Granite-4.1-3B?
Download from HuggingFace, review the GitHub repository and Granite Docs, and start with the provided generation and tool-calling examples. For production deployments, assess hardware requirements, finetune for your domain, and conduct security validation. IBM enterprise support may be available separately.