granite-3.0-1b-a400m-base
Granite-3.0-1B-A400M-Base is a 1.3B parameter decoder-only language model from IBM, released October 2024. It uses a sparse Mixture of Experts architecture with only 400M active parameters, making it computationally efficient. Trained on 10 trillion tokens across diverse domains (web, code, academic, books, math) in two stages, it supports text generation tasks like summarization, classification, and question-answering. It operates under Apache 2.0 license with no gating, making it freely available for commercial use. The model is not safety-aligned and the developers explicitly note it may produce problematic outputs.
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 | 1.4B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 71.6k |
| Likes | 7 |
| Last updated | 2024-12-19 |
| Source | ibm-granite/granite-3.0-1b-a400m-base |
What granite-3.0-1b-a400m-base is
Decoder-only sparse MoE transformer with 24 layers, 16 attention heads, 32 experts (8 selected per token), 4096-token context window, RoPE position embeddings, and SwiGLU activation. Trained on Blue Vela (NVIDIA H100 cluster) using 100% renewable energy. Two-stage training: Stage 1 on 8T diverse-domain tokens, Stage 2 on 2T curated high-quality tokens including multilingual and instruction data. Supports 12 languages natively. No safety alignment applied.
Run granite-3.0-1b-a400m-base 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-3.0-1b-a400m-base")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: ~2.6 GB VRAM (fp16) to ~5.2 GB (fp32) for full model inference; 1.6 GB active compute per token (400M params, fp16). Batch inference on CPU feasible but slow. GPU recommended: NVIDIA RTX 3090, RTX 4090, or cloud equivalent (e.g., AWS g4dn, A100). Quantized versions (int8, int4) reduce footprint further but require additional tooling. Requires verification on target hardware.
Model card does not specify LoRA, QLoRA, or full fine-tuning guidelines. Given MoE architecture (32 experts, 8 routed per token), LoRA adapters on the dense layers (attention, MLP) are plausible but expert-level LoRA is non-standard and requires custom implementation. Full fine-tuning is feasible on a single high-end GPU or multi-GPU setup. Authors encourage fine-tuning for unsupported languages and domain adaptation. Requires empirical testing on your target task.
When to avoid it — and what to weigh
- You Require Guaranteed Safety Alignment — The model card explicitly states no safety alignment has been applied and warns of potential problematic outputs. If your application requires guaranteed non-toxic, bias-mitigated, or adversarially-robust responses, this base model is unsuitable without additional safety fine-tuning.
- You Need Out-of-the-Box Instruction Following — This is a base model, not an instruction-tuned variant. Direct prompting for complex reasoning or structured outputs will likely underperform compared to chat/instruction models. Expect to fine-tune or use prompt engineering as a workaround.
- You Depend on Long-Context Reasoning (>4K tokens) — Maximum context length is 4096 tokens. If your workload involves summarizing long documents, extensive in-context learning, or retrieval-augmented generation with large retrieved passages, this context window is limiting.
- You Need Proven Hallucination Mitigation — The model card notes uncertainty about hallucination susceptibility in smaller models and identifies it as an active research area. No empirical evaluation or mitigation strategy is provided for verbatim training-data reproduction or factual accuracy.
License & commercial use
Apache 2.0 (OSI-approved, permissive open-source license). Grants rights to use, modify, distribute, and sublicense freely, provided copyright and license notices are retained and liability is disclaimed.
Apache 2.0 explicitly permits commercial use, including closed-source products and proprietary services, with no royalty obligations. You may use this model in a paid SaaS, enterprise software, or commercial product without restriction or license fees, provided you include the Apache 2.0 license notice. No additional commercial endorsement, warranty, or support from IBM is implied.
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 |
No formal security audit, red-teaming results, or vulnerability disclosures are mentioned. Model is unaligned and acknowledges risk of problematic outputs (bias, misinformation, malicious use). Inference in sandbox/isolated environments recommended if handling untrusted prompts. Training data includes open-source and proprietary sources; attribution details in technical report (requires review). No privacy-preserving training method disclosed. Users responsible for content filtering, audit logging, and responsible deployment.
Alternatives to consider
TinyLLaMA (1.1B dense transformer)
Similar parameter count, fully dense architecture (simpler deployment), permissive license. Trade-off: no MoE efficiency; slightly smaller. Better for edge/mobile if sparsity overhead is a concern.
Phi-2 (2.7B) or Phi-3 (3.8B)
Slightly larger, instruction-tuned by Microsoft, MIT license. Stronger out-of-the-box instruction following and chat capability. Trade-off: larger model size; no explicit sparse routing.
Mistral-7B or Mistral Small
Larger, instruction-tuned, strong industry adoption, Apache 2.0. Better for production chat/QA without fine-tuning. Trade-off: 5–7× parameter count and VRAM footprint.
Ship granite-3.0-1b-a400m-base with senior software developers
Start with private/self-hosted deployment for low-latency inference, or integrate into a custom LLM application. Review the model card's ethical considerations, plan safety alignment if needed, and benchmark on your hardware. Explore fine-tuning for domain-specific tasks.
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granite-3.0-1b-a400m-base FAQ
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Is this model safe to use for production without additional alignment?
Can I fine-tune this model for my language or task?
Work with a software development agency
Adopting granite-3.0-1b-a400m-base is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source llms software in production.
Ready to Deploy Granite-3.0-1B-A400M-Base?
Start with private/self-hosted deployment for low-latency inference, or integrate into a custom LLM application. Review the model card's ethical considerations, plan safety alignment if needed, and benchmark on your hardware. Explore fine-tuning for domain-specific tasks.