Open-Weight LLM · Private & Custom AI
granite-4.0-micro
3B instruction-tuned model purpose-built for enterprise ops automation, tool-calling agents, and private deployment in mid-market companies.
Granite-4.0-Micro is a 3.4B parameter instruct model from IBM optimized for instruction-following, function-calling, and multi-lingual workflows. It's production-ready for ops teams deploying custom AI agents, automating document processing, and running on-premise LLM infrastructure without data egress.
Model facts
Private deployment
Run granite-4.0-micro in your own environment
Self-hostable on modest GPU hardware (~8–16GB VRAM in float16). No API dependency; model weights and tokenizer load directly via Hugging Face. Company retains full data residency—all inputs, outputs, and inference logs stay within the customer's environment. Supports Azure and standard transformer deployment stacks.
Operational AI use cases
Structured Document & Form Processing
Automate extraction from contracts, invoices, compliance forms. Text extraction and classification capabilities allow ops teams to route documents, flag exceptions, and populate internal systems without manual review—keeping sensitive docs in private infrastructure.
Internal Knowledge Q&A & Support Deflection
Deploy as the backbone of a private knowledge bot (RAG-enabled) to answer employee questions on policy, benefits, procedures, and internal wikis. Reduces support ticket volume while keeping Q&A data and logs on-premise.
Function-Calling Workflow Automation
Use enhanced tool-calling to trigger CRM updates, ticket creation, approval workflows, and reporting pipelines based on natural language requests. Operators submit tasks in plain English; model generates correct API calls to internal systems.
Custom AI
As a base for custom AI
Strong foundation for vertical-specific AI applications. IBM's hybrid training (supervised finetuning + RL alignment + model merging) means the base is trainable for domain-specific tasks (legal, medical, financial) while keeping model size lean and deployable. Finetuning and merging techniques are documented for enterprise customization.
In the operating system
Where it fits
Knowledge/retrieval layer (RAG integrations, document QA) and agentic reasoning layer (tool-calling, workflow orchestration). Compact enough to run alongside vector stores and API gateways without ballooning ops infrastructure cost. Sits cleanly between ETL pipelines and downstream business systems.
Data control & security
Private self-hosted deployment ensures zero data transmission to external APIs or third parties. Inference logs, user queries, and generated responses remain within your network boundary—critical for HIPAA, financial, legal, or competitive-sensitive workflows. No telemetry home-phoning; no model improvement via your data. Architecture choice, not a claim about the model itself; standard security practices (network isolation, access controls, encryption at rest) remain the customer's responsibility.
Hardware footprint
**Estimate (unverified).** Float16: ~7GB VRAM. Int8 quantized: ~4GB. Int4 quantized: ~2GB. Batch inference (8–16 concurrent requests) on single A100 (40GB) feasible. CPU-only inference possible but slow (~seconds per token); GPU strongly recommended for ops SLAs.
Integration
Drop-in via transformers/vLLM (no special SDKs required). Chat template and tool-calling follow OpenAI function schema—easing wiring into existing agentic frameworks. Tokenizer compatible with standard Hugging Face pipelines. Deploy on Kubernetes, Docker, or bare metal; Azure tagging indicates compatibility with Azure ML. Inference latency suitable for real-time ops workflows (100–300ms per request on A100; varies by quantization).
When it's not the right fit
- —Long context reasoning required—context length not disclosed; smaller model may hit token limits on multi-document analysis or long chains of thought.
- —Multilingual edge cases—supported languages listed (12) but finetuning/custom languages require end-user effort; not a ready-to-use polyglot for all markets.
- —Latency-critical real-time interactions—3B model trades some speed for self-hostability; compare against larger models (7B+) if sub-100ms response time is non-negotiable.
- —Bleeding-edge code generation—positioned for general tasks, not specialized coding; Llama-Code or Granite code-specific variants may outperform for software engineering ops.
Alternatives to consider
Llama 3.2 1B / 3B
Meta's comparable size tier, permissive Llama 2.0 license, strong ops baseline. Smaller (1B) variant saves compute; 3B closer to Granite-Micro in capability. Less enterprise-tuned; tool-calling not as documented.
Phi-3.5 Mini (3.8B)
Microsoft open-weight competitor, similar parameter count, instruction-tuned for enterprise. MIT license; strong instruction-following. Context length (128K) superior; tool-use support less mature than Granite.
Mistral 7B Instruct
Larger footprint but stronger reasoning and code capability. Apache 2.0 license, widely deployed. Better for complex multi-step workflows; requires ~16GB VRAM, less suitable for resource-constrained ops infrastructure.
Related open models
FAQ
Can I run Granite-4.0-Micro on my own servers without contacting IBM or sending data to the cloud?
Yes. Download weights from Hugging Face, load via transformers, and run inference entirely on-premise. No API key, cloud service, or vendor lock-in required. Your data never leaves your infrastructure.
What's the commercial-use license status?
Apache 2.0: permissive, allows commercial products, modifications, and redistribution. You can build and sell AI products using this model without royalties or license fees. Review your legal team for compliance with your end-user agreements.
Can I fine-tune Granite-4.0-Micro on our proprietary data?
Yes. Model card documents supervised finetuning and model-merging techniques. Fine-tuning remains your intellectual property; IBM has no rights to it. Requires GPU compute and ML ops tooling; feasible for mid-market teams with dedicated AI engineering.
What if we need multilingual support beyond the 12 listed languages?
Model card states users may fine-tune for other languages. No pre-baked support; custom data and training required. Alternatively, consider pairing with a dedicated multilingual model or building a language-specific adapter layer.
Build Your Private AI Operating System
Granite-4.0-Micro is ready for custom AI workflows in your environment. Work with LLM.co to integrate it into your ops stack, automate departmental tasks, and own your LLM infrastructure. Start building.