granite-4.1-3b-GGUF
Granite 4.1 3B is a lightweight, open-source language model from IBM available in GGUF format with multiple quantization options. It is designed for text generation tasks and can run on resource-constrained hardware. The model is unmodified from the base, permissively licensed under Apache 2.0, and not gated, making it accessible for both research and commercial use.
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 | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 151.2k |
| Likes | 5 |
| Last updated | 2026-04-20 |
| Source | ibm-granite/granite-4.1-3b-GGUF |
What granite-4.1-3b-GGUF is
GGUF-quantized variant of IBM's Granite 4.1 3B base model. GGUF format enables efficient inference via llama.cpp and similar engines with reduced memory footprint. Parameter count and exact context length not disclosed in this model card; refer to base model documentation. Last updated April 2026. No information provided on quantization levels available in this specific repository.
Run granite-4.1-3b-GGUF 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-GGUF")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 (verify against base model and your chosen quantization): 3B dense model in FP16 ≈ 6–8 GB VRAM. GGUF quantization (likely Q4_0 or Q5_K_M) reduces this to 1.5–3 GB VRAM. Inference possible on CPU (slow), but GPU acceleration (NVIDIA, AMD, Metal) recommended. Exact quantization levels and per-level VRAM in this repo Unknown.
GGUF is an inference-optimized format; training/fine-tuning typically requires working with the base .safetensors model. LoRA/QLoRA fine-tuning of base model is feasible given 3B size, but not directly supported on GGUF. Requires conversion back to PyTorch for training or use of quantization-aware training frameworks. Check base model repository for fine-tuning guidance.
When to avoid it — and what to weigh
- Complex Reasoning or Multi-Step Problem Solving — 3B models have limited capacity for intricate logic, math, coding, or reasoning tasks compared to larger models. Not suitable for applications requiring high accuracy on complex domain problems.
- High Throughput, Multi-Tenant Inference at Scale — While efficient, a 3B model may not deliver the latency/quality balance needed for large-scale commercial API services. Consider larger models or ensemble approaches for production SaaS.
- No Clear Domain Expertise Expected — Base model card and pre-training details not provided in this excerpt. Avoid without first reviewing base model documentation to confirm domain coverage matches your use case.
- Strict Compliance or Audit Requirements for Model Provenance — GGUF is a converted format; full training data, fine-tuning, and safety audit details must be sourced from IBM's base model card. Quantization process transparency Unknown.
License & commercial use
Apache License 2.0 (OSI-approved, permissive). Permits commercial use, modification, distribution, and private use with conditions: preservation of copyright/license notices and state changes. No liability or warranty provided by licensor.
Apache 2.0 is a permissive, OSI-approved license that explicitly permits commercial use, including proprietary applications, as long as copyright notices and license text are retained and changes are documented. No gating, no commercial restriction. However, verify IBM's stance on support, indemnification, and liability for production deployments; Apache 2.0 offers no warranty. Recommend legal review for enterprise contracts.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Limited |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | Medium |
GGUF is a binary serialization format; vulnerability surface depends on llama.cpp and serving framework implementations. Quantized models may have different adversarial robustness than FP16 originals; testing recommended for sensitive use cases. No model card mention of safety training, bias mitigation, or adversarial testing. Obtain base model safety documentation from IBM before production deployment. Use inference in a sandboxed environment if untrusted prompts are expected.
Alternatives to consider
Mistral 7B (GGUF variant)
Larger (7B), stronger reasoning, same permissive license (Apache 2.0), and multiple quantized distributions. Better for general-purpose tasks; higher resource cost.
Phi 3 Mini (Microsoft)
Similar size class (3.8B), optimized for efficiency, MIT-licensed, good instruction-following. Slightly different training/domain; consider if Microsoft ecosystem alignment matters.
Llama 2 7B (Meta, GGUF available)
Larger, stronger baseline, permissive license (Llama 2 Community License). More adoption and ecosystem support; verify commercial terms carefully as Llama 2 license differs from Apache 2.0.
Ship granite-4.1-3b-GGUF with senior software developers
Ready to test this lightweight model? Start with a local deployment using llama.cpp or Ollama, then assess memory, latency, and quality on your workloads. Review the base model card on Hugging Face for domain coverage and safety details before production use.
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granite-4.1-3b-GGUF FAQ
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Custom software development services
Need help beyond evaluating granite-4.1-3b-GGUF? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Evaluate Granite 4.1 3B for Your Infrastructure
Ready to test this lightweight model? Start with a local deployment using llama.cpp or Ollama, then assess memory, latency, and quality on your workloads. Review the base model card on Hugging Face for domain coverage and safety details before production use.