granite-4.1-30b
Granite-4.1-30B is a 30 billion parameter open-source LLM from IBM, released April 2026, optimized for instruction-following, tool calling, and multilingual tasks. It is permissively licensed under Apache 2.0, not gated, and suitable for self-hosted or cloud deployment in business and AI agent applications.
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 | 28.9B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 77.9k |
| Likes | 136 |
| Last updated | 2026-05-04 |
| Source | ibm-granite/granite-4.1-30b |
What granite-4.1-30b is
A 28.9B parameter dense transformer model fine-tuned from Granite-4.1-30B-Base using supervised fine-tuning and reinforcement learning alignment on permissively-licensed instruction datasets and synthetic data. Supports 12 languages, includes enhanced tool-calling scaffolding (OpenAI function schema compatible), and evaluates favorably on MMLU (80.16), MMLU-Pro (64.09), and BBH (83.74) benchmarks at the 30B tier.
Run granite-4.1-30b 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-4.1-30b")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 for full precision (fp32): ~115–120 GB VRAM; fp16: ~57–60 GB VRAM; int8 quantization: ~28–30 GB VRAM; int4 (GPTQ/AWQ): ~8–15 GB VRAM. Most practical deployments use bf16 or quantized variants on high-end GPUs (A100, H100) or multi-GPU clusters. CPU inference is feasible but slow. Context length is not specified; verification against source documentation recommended.
Model is fine-tunable via standard HuggingFace Transformers API. LoRA and QLoRA are plausible given the dense architecture and availability of community tooling. Model card does not explicitly document fine-tuning guidance (e.g., learning rates, data requirements, or instruction-format recommendations); practitioners should reference the Granite technical blog and GitHub repository for detailed recipes.
When to avoid it — and what to weigh
- Latency-Critical or Resource-Constrained Edge Deployment — At 30B parameters, inference memory footprint is substantial (estimate: 60–120 GB depending on precision). Poorly suited for real-time mobile, embedded, or extremely cost-sensitive edge endpoints without quantization and optimization.
- Specialized Domains Requiring Domain-Specific Pretraining — Granite-4.1-30B is a general-purpose instruction model. Highly specialized use cases (e.g., genomics, proprietary scientific domains) may require domain-adaptive pretraining, which is orthogonal to this general foundation.
- Guaranteed Model Stability or Long-Term Support SLA — Release date is April 29, 2026 (recent). No documented long-term support roadmap, stability guarantees, or update schedule. Organizations requiring contractual SLAs should consider IBM's commercial products.
- Regulatory Compliance with Black-Box Requirements — Open-source models provide transparency but may not satisfy audit or compliance regimes that mandate vendor-backed responsibility, indemnification, or formal model evaluation reports.
License & commercial use
Apache License 2.0 (fully permissive OSI-approved license). Permits commercial use, modification, and redistribution under the terms of Apache 2.0: retain license and copyright notice, provide modifications under the same license, and include NOTICE file if present.
Apache 2.0 explicitly permits commercial use without royalty or vendor permission. You may build commercial products, SaaS offerings, and proprietary applications using Granite-4.1-30B, provided you include the Apache 2.0 license and attributions in derivative works. No gating or commercial-use restrictions apply. For enterprise support, SLAs, or liability protection, contact IBM separately—this open-source release includes no warranty or indemnification.
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 |
As an open-source model, security assurance depends on user deployment and integration practices, not vendor controls. Consideration points: (1) Model behavior is not formally verified; misuse risk (e.g., prompt injection, jailbreak) depends on application design. (2) Training data sources (permissive open datasets + synthetic IBM data) reduce but do not eliminate risk of inheriting biases or undesired patterns. (3) No published adversarial robustness or safety evaluation results in card. (4) Deployment security (RBAC, data isolation, inference logging) is operator responsibility. Practitioners should integrate input validation, output filtering, rate limiting, and monitoring.
Alternatives to consider
Meta Llama-2 / Llama-3 series
Similar scale and instruction-tuning, broadly adopted, strong community support. Llama-2 uses custom research license (not OSI-standard); Llama-3 is more permissive. Comparable benchmarks; larger ecosystem for deployment tooling.
Google Gemma (2B / 7B) or Mistral 7B
Smaller footprint, faster inference, lower resource requirements. Suitable if 30B scale is overkill. Mistral 7B offers competitive quality per parameter. Trade-off: less multilingual support than Granite-4.1.
IBM Granite-4.1-8B or Granite-4.1-3B
Same license and training philosophy; smaller models reduce inference cost and latency. Appropriate for resource-constrained deployments; 8B is often sweet-spot for quality/speed trade-off. Benchmarks show 8B achieves 73.84 on MMLU vs. 80.16 for 30B.
Ship granite-4.1-30b with senior software developers
Granite-4.1-30B is production-ready for private or cloud deployment. Start with our self-hosted LLM or custom AI app templates to integrate fine-tuned Granite into your data pipelines, agents, or RAG systems. Contact our team for architecture guidance on multi-GPU serving, quantization strategies, and compliance requirements.
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granite-4.1-30b FAQ
Can I use Granite-4.1-30B in a commercial product without paying IBM?
What is the estimated VRAM requirement for inference?
How does Granite-4.1-30B compare to Llama-2 70B or GPT-3.5?
Does the model support function calling out-of-the-box?
Software developers & web developers for hire
DEV.co helps companies turn open-source tools like granite-4.1-30b into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source llms stack.
Ready to Deploy Granite-4.1-30B?
Granite-4.1-30B is production-ready for private or cloud deployment. Start with our self-hosted LLM or custom AI app templates to integrate fine-tuned Granite into your data pipelines, agents, or RAG systems. Contact our team for architecture guidance on multi-GPU serving, quantization strategies, and compliance requirements.