Granite 3 MoE (3B active)
Granite MoE shape. 16B total / 3B active. Workstation-deployable; the IBM enterprise alternative to Qwen / DeepSeek small MoEs.
Overview
Granite MoE shape. 16B total / 3B active. Workstation-deployable; the IBM enterprise alternative to Qwen / DeepSeek small MoEs.
How to run it
Granite 3 MoE 3B-Active is IBM's Mixture-of-Experts model with ~3B active parameters per token (total parameters ~10-15B). Designed as an ultra-efficient MoE — tiny active footprint with surprising quality. Run at Q4_K_M via Ollama (ollama pull granite3-moe:3b) or llama.cpp with -ngl 999 -fa -c 8192. Q4_K_M file size ~6-8 GB on disk. Minimum VRAM: 6 GB — RTX 2060 (6GB) at Q4_K_M with expert offload. RTX 3060 12GB: Q4_K_M with all experts in VRAM, comfortable. Recommended: any GPU with 8+ GB at Q4_K_M. Throughput: ~60-100+ tok/s on RTX 4090 at Q4_K_M — flies due to tiny active subset. Granite architecture — IBM's design, verify llama.cpp support. Granite 3 is IBM's enterprise-focused model family, optimized for business tasks: summarization, classification, extraction, RAG. The MoE variant adds quality at minimal compute cost — 3B active means it runs on phones, Raspberry Pi 5, and low-end GPUs. Use for: edge deployment, high-throughput classification, lightweight RAG, CPU-only inference. Not for: complex reasoning, creative writing, long-form generation — 3B active is still a small model. Context: 8K advertised; practical at Q4 is 8K on any 8+ GB device.
Hardware guidance
Minimum: 4 GB RAM CPU-only at Q4_K_M (~3-6 tok/s) or Raspberry Pi 5 8GB at Q4. Recommended: any GPU with 6+ GB VRAM at Q4_K_M. VRAM math: ~10-15B total, ~3B active. Q4_K_M ≈ 6-8 GB for full weights. Expert offload: ~2-3 GB active experts in VRAM. KV cache at 8K: ~1-2 GB. Total with all experts in VRAM: ~8-10 GB — fits 10+ GB GPUs easily. RTX 2060 6GB: Q4 with expert offload. RTX 3060 12GB: all experts on-GPU, fast. RTX 4090 24GB: laughably over-provisioned — runs at 100+ tok/s. CPU-only on modern laptop: 5-10 tok/s. This is one of the most deployable models — runs on almost anything. Target for edge/IoT/CPU-only deployments.
What breaks first
- 3B active ceiling. Despite the MoE architecture, 3B active parameters has fundamental quality limits. Complex reasoning, nuanced instruction-following, and deep knowledge recall hit the small-model wall. 2. Enterprise license. IBM's Granite license may differ from standard open-weight licenses. Verify commercial use terms — IBM typically uses permissive licenses but verify for Granite 3 specifically. 3. Granite architecture support. IBM's architecture may not be standard Llama. Verify llama.cpp support before deploying. 4. Quantization overkill. At this size, Q8 is only ~12-15 GB — if you have the VRAM, use Q8 for maximum quality. The file size penalty is small at this scale.
Runtime recommendation
Common beginner mistakes
Mistake: Expecting Granite 3 MoE to match 7B+ dense models. Fix: 3B active is the quality ceiling. The model punches above its weight but doesn't match 7B+ models. Test your task. Mistake: Over-provisioning hardware. Fix: 6 GB VRAM is plenty for all experts at Q4. You don't need an RTX 4090 for this — it works on integrated graphics, phones, and Raspberry Pi. Mistake: Using Q3 when Q8 fits. Fix: Q8 is only ~12-15 GB. If your GPU has 12+ GB, just use Q8 for maximum quality. The file size difference at this scale is small. Mistake: Assuming Granite supports standard Llama chat templates. Fix: IBM's Granite uses its own chat template. Verify on the hf repo.
Family & lineage
How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.
Strengths
- Apache 2.0
- MoE efficiency
Weaknesses
- Smaller community than Mixtral / Qwen MoEs
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 9.5 GB | 12 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Granite 3 MoE (3B active).
Frequently asked
What's the minimum VRAM to run Granite 3 MoE (3B active)?
Can I use Granite 3 MoE (3B active) commercially?
What's the context length of Granite 3 MoE (3B active)?
Source: huggingface.co/ibm-granite/granite-3-moe-3b
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Granite 3 MoE (3B active) runs on your specific hardware before committing money.