Granite 3 MoE (3B active)
Granite MoE shape. 16B total / 3B active. Workstation-deployable; the IBM enterprise alternative to Qwen / DeepSeek small MoEs.
Positioning
IBM's Granite 3 MoE (3B active) is a 16B-parameter Mixture-of-Experts model with approximately 3B parameters activated per token. Released under the permissive Apache 2.0 license, it targets enterprise users who need a local, consumer-deployable model with IBM's backing. Its architecture makes it distinct in the open-weight landscape as a workstation-class MoE that balances total parameter count with inference efficiency.
Strengths
- Enterprise-grade license: Apache 2.0 permits commercial use, modification, and redistribution without restrictions, making it suitable for proprietary deployments.
- Efficient MoE architecture: With 16B total parameters but only ~3B active per token, inference compute cost is closer to a dense 3B model, enabling deployment on consumer hardware.
- Large context window: 131,072 tokens of context support long-document analysis, codebase understanding, and multi-turn conversations without truncation.
- Multiple quantized options: From FP16 (32 GB) down to Q2_K (5.2 GB), the model fits a wide range of GPU memory budgets, with Q4_K_M (~9.0 GB) being a practical balance for many consumer GPUs.
Limitations
- No community-verified benchmarks: We do not have independent measurements for this model. Published vendor metrics should be treated as best-case until third-party validation appears.
- Quantization overhead: KV cache and framework overhead can add 30-50% to memory requirements at typical context lengths, meaning a Q4_K_M model may need ~12-14 GB total for comfortable operation.
- MoE routing overhead: While active parameters are low, the full 16B model must be loaded into memory, and the routing mechanism adds latency compared to a dense model of similar active size.
- Limited ecosystem: As a newer entry from IBM, community tooling, fine-tuning recipes, and deployment guides may be less mature than for more established open-weight models.
What it takes to run this locally
At FP16, the model requires 32 GB of disk space and roughly 32 GB of GPU memory, placing it in the workstation class (single 48 GB GPU or dual 24 GB GPUs). Quantized versions reduce memory needs: Q8_0 (17 GB), Q6_K (13.2 GB), Q5_K_M (11.4 GB), Q4_K_M (9.0 GB), Q3_K_M (7.8 GB), and Q2_K (~5.2 GB). For consumer GPUs with 12-24 GB VRAM, Q4_K_M or Q5_K_M are practical starting points, but users must account for ~30-50% additional memory for KV cache and framework overhead at typical context lengths.
Should you run this locally?
Yes if you need a permissively licensed MoE model for enterprise use cases and have a workstation-class GPU (or a consumer GPU with sufficient VRAM for quantized versions). The architecture's efficient active-parameter count makes it suitable for interactive applications where latency matters.
No if you require verified benchmark data for procurement decisions, or if your deployment must fit entirely within a single consumer GPU without quantization — the FP16 model exceeds 24 GB VRAM. Also consider whether the smaller ecosystem compared to more popular MoE models is acceptable for your workflow.
Catalog cross-links
- Granite 3 MoE (3B active)
- IBM Granite family
- Consumer GPU guide
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).
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
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.