granite-4.1-8b
Granite-4.1-8B is an 8-billion-parameter instruction-tuned language model from IBM, released April 2026. It supports 12 languages, excels at tool-calling, summarization, code tasks, and RAG workflows. Licensed under Apache 2.0, it is available openly on HuggingFace without gating. The model shows strong benchmark performance (MMLU 73.84, BBH 80.51) and is suitable for business applications and AI agents.
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 | 8.8B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 668.1k |
| Likes | 208 |
| Last updated | 2026-05-04 |
| Source | ibm-granite/granite-4.1-8b |
What granite-4.1-8b is
Granite-4.1-8B is a dense transformer LLM with 8.79B parameters, fine-tuned from Granite-4.1-8B-Base using open-source instruction datasets and synthetic data. Post-training includes supervised fine-tuning and reinforcement learning alignment. The model supports chat templates with tool-calling via OpenAI function schema, fill-in-the-middle (FIM) code completion, and multilingual inference. Context length is not specified in the card. Model is compatible with standard transformers library and supports deployment on Azure.
Run granite-4.1-8b 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-4.1-8b")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
Estimated 16–20 GB VRAM for inference in full precision (bfloat16). Typical deployment: single NVIDIA A100 (40GB) or A6000 (48GB), or multiple smaller GPUs with device_map. Quantization (INT8, GPTQ) can reduce to ~8–12 GB. CPU inference is feasible for lower throughput use cases. No official specs provided; verify with your target hardware.
LoRA and full fine-tuning are feasible; model is open-sourced and supports standard transformers training pipelines. Card explicitly states users can fine-tune for languages beyond the 12 supported. QLoRA on consumer GPUs (24GB) is practical. No proprietary training infrastructure lock-in.
When to avoid it — and what to weigh
- Unknown Context Length Requirements — Model card does not specify context window length. If your application requires guaranteed long-context processing (e.g., 32K+ tokens), verify against internal benchmarks or contact IBM before committing.
- Extreme Low-Latency Inference at Scale — 8B dense model is computationally heavier than quantized or smaller alternatives. If sub-10ms per-token latency is critical at high QPS, consider smaller dense models or GGUF/quantized variants.
- Real-Time Audio/Vision Fusion — This is a text-only model. Does not support multimodal input (images, audio). Use alternative multimodal models (e.g., LLaVA, GPT-4V) if visual or audio understanding is required.
- Proprietary Closed-Source Requirements — Apache 2.0 licensed open-source model. If your compliance mandate forbids open-source dependencies or requires proprietary vendor support SLAs, evaluate closed alternatives.
License & commercial use
Apache License 2.0 (OSI-approved permissive open-source license). Permits commercial use, modification, and distribution with minimal restrictions (retain license header and copyright notice). No patent grants or liability disclaimers unique to LLM context.
Apache 2.0 is a permissive OSI license that explicitly permits commercial use. You may use, modify, and redistribute Granite-4.1-8B in proprietary applications without seeking IBM permission, provided you retain the Apache 2.0 license header. However: (1) no warranty or SLA from IBM is implied; (2) model card does not disclose liability caps or indemnification; (3) review IBM's Granite website and GitHub for any service-level or support terms. For regulated industries (finance, healthcare), conduct additional legal and security due diligence.
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 |
Model card does not disclose: adversarial robustness testing, jailbreak resistance, prompt injection mitigations, or known vulnerabilities. Card mentions 'professional, accurate, and safe responses' via system prompt tuning but provides no security audit or red-teaming results. Recommendations: (1) assume standard LLM attack surface (prompt injection, data leakage via generation); (2) do not use for security-critical decisions without additional validation; (3) review IBM Granite security docs and perform your own pen-testing for regulated domains; (4) sanitize user inputs and implement output guardrails in production.
Alternatives to consider
Meta Llama 3.1 (8B)
Similar 8B parameter dense model, Apache 2.0 licensed, strong tool-calling support. Widely deployed; consider if you prefer Meta's backing or need stronger community integrations.
Mistral 7B
Smaller (7B), higher efficiency, Apache 2.0 licensed. Good trade-off if latency/cost is prioritized over absolute capability; lacks explicit multilingual + tool-calling emphasis.
IBM Granite 30B (Dense)
Same family, larger parameter count, higher benchmark scores (MMLU 80.16). Choose if accuracy is non-negotiable and you have sufficient GPU resources.
Ship granite-4.1-8b with senior software developers
Start with the code examples in the model card. Verify context length and hardware fit for your use case. Deploy via vLLM, TGI, or Azure. For production, review IBM's Granite docs and conduct security assessment.
Talk to DEV.coRelated open-source tools
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granite-4.1-8b FAQ
Can I use Granite-4.1-8B in a commercial product without paying IBM?
What GPU do I need to run this model?
Does Granite-4.1-8B support long-context (32K+ tokens)?
Can I fine-tune this model on my own data?
Work with a software development agency
From first prototype to production, DEV.co delivers software development services around tools like granite-4.1-8b. 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-4.1-8B?
Start with the code examples in the model card. Verify context length and hardware fit for your use case. Deploy via vLLM, TGI, or Azure. For production, review IBM's Granite docs and conduct security assessment.