Open-Weight LLM · Private & Custom AI
granite-4.0-tiny-preview
A 7B MoE instruct model for private-hosted ops workflows—reasoning, summarization, doc handling, and function-calling in your own infrastructure.
Granite-4.0-Tiny-Preview is IBM's Apache-2.0 licensed 7B mixture-of-experts model, fine-tuned for instruction-following across 12 languages with support for long-context tasks. For ops teams, it's a self-controllable foundation for automating support tickets, document processing, meeting notes, and internal knowledge workflows without shipping data to third parties.
Model facts
Private deployment
Run granite-4.0-tiny-preview in your own environment
Self-hosting requires PyTorch + transformers (v4.45+), ~14–27 GB VRAM depending on precision (bfloat16 recommended). Deploy on your own GPU cluster, Kubernetes, or containerized on-prem infrastructure. Data stays in your environment entirely—no third-party inference, no usage logging to IBM. Model card notes training on permissive-licensed and synthetic data; no proprietary training artifacts disclosed.
Operational AI use cases
Support ticket routing & summarization
Ingest incoming support tickets, auto-classify by department, extract key entities (issue type, customer ID, severity), and generate internal summaries. Long-context capability handles multi-message threads. Run entirely on private infrastructure; customer conversations never leave your network.
Internal document & meeting QA agent
Index internal wikis, meeting transcripts, and compliance docs. Use Granite-4.0-Tiny with RAG (retrieval-augmented generation) to answer employee questions: 'What's our data retention policy?' 'Who owns the API docs?' Structured chat format and function-calling enable agent workflows; all queries and knowledge bases stay private.
Accounts/Finance workflow automation
Process invoices, expense reports, and contract text: extract line items, classify GL codes, flag anomalies. Text extraction and classification tasks rated in model card. Chain with your existing accounting system APIs via function-calling. No external API calls; process sensitive financial data on-site.
Custom AI
As a base for custom AI
Strong foundation for custom AI products. Fine-tune on domain-specific instruction data (your SOP docs, past tickets, internal terminology) or use as-is for chat/reasoning. MoE architecture provides efficiency for batched inference. Apache-2.0 license permits commercial product wrapping. Function-calling support enables agent integration into internal tools (Slack bots, helpdesk integrations, form auto-fillers).
In the operating system
Where it fits
Base reasoning layer in a knowledge/workflow stack. Sits below custom retrieval (RAG) and agent orchestration (LangChain, LlamaIndex). Feeds outputs to task-specific APIs (ticketing, CRM, document management). In LLM.co terms: the 'ops AI brain' that understands language; layer above retrieval, below agent routing logic.
Data control & security
Private deployment means all inference stays on your infrastructure—no data leaves your network, no vendor visibility into queries or internal knowledge. No inherent security guarantees from the model itself; standard responsibility for securing containers, access controls, and GPU infrastructure applies. Audit training data sources (IBM discloses permissive + synthetic) to align with compliance needs (GDPR, HIPAA, etc.).
Hardware footprint
Estimated VRAM (inference): ~14 GB (bfloat16, single GPU), ~27 GB (float32). For serving multiple concurrent requests, allocate 2–4 H100s or A100s (40GB+) depending on batch size and latency SLA. MoE design may reduce compute vs. dense 7B in some scenarios.
Integration
Load via HuggingFace transformers; supports vLLM, TensorRT-LLM, and Ollama for inference serving. Chat template uses structured role/content format; integrate via REST wrappers (FastAPI, TorchServe) or direct Python API. Function-calling schema available; wire to internal APIs (Jira, Salesforce, document DBs) via orchestration layer. Tokenizer is standard; no custom preprocessing needed.
When it's not the right fit
- —You need sub-50ms latency at scale—7B inference, even sparse, requires GPU and careful batching; cloud APIs offer better p99 guarantees.
- —Real-time multilingual NLP for languages beyond the 12 supported (English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, Chinese). Fine-tuning required; not zero-shot.
- —Your ops team cannot provision/maintain GPU infrastructure or Kubernetes clusters; hosting complexity may outweigh data-control benefits vs. managed private-AI services.
- —You require certified model explainability or formal security audit trails; IBM's model card does not disclose formal security certifications or bias testing frameworks.
Alternatives to consider
Llama 3.1 8B (Meta, Apache 2.0)
Larger ecosystem, faster inference iteration, no MoE complexity. Trade-off: slightly less explicit long-context tuning; requires more VRAM.
Mistral 7B (Mistral AI, Apache 2.0)
Well-established, sharp ops-AI adoption, strong RAG benchmarks. Trade-off: smaller context window, less internal-tool tuning than Granite.
Qwen2 7B (Alibaba, Apache 2.0)
Multilingual parity (12+ languages), competitive reasoning scores. Trade-off: Chinese-tuned; may require rebalancing for Western ops use cases.
Related open models
FAQ
Can we run this on-premises without a data center?
Yes, via containerized GPU instances (on your own hardware, co-location, or private cloud like AWS Outposts). You'll need GPU(s) and orchestration tooling. No real datacenter required, but ops overhead increases vs. cloud APIs.
Is Granite-4.0-Tiny free to use commercially?
Yes. Apache 2.0 permits commercial use, modification, and distribution. No license royalties or usage reporting to IBM. Attribution required in derivative products.
How do we fine-tune this for our internal domain (e.g., custom terminology)?
Standard supervised fine-tuning: collect 100–1000 examples (user prompt + ideal response) in your domain, use HuggingFace Trainer or vLLM LoRA. No special license restrictions. IBM's model card notes training on synthetic data; your custom data can be proprietary.
What's the difference between Granite-4.0-Tiny and the base model?
Tiny-Preview is the instruct-tuned variant; fine-tuned with SFT and RLHF for structured chat and reasoning. Base is raw. Use Preview for ops workflows; Base only if you're doing further fine-tuning yourself.
Build a Private Ops AI System
Granite-4.0-Tiny is a sharp foundation for internal automation—support routing, knowledge QA, compliance workflows—running entirely on your infrastructure. LLM.co helps you integrate it into your ops stack: custom fine-tuning, RAG, agent routing, and API wiring. Explore how to move from one-off prompting to a unified AI operating system.