Granite 4.1 30B Instruct
Granite 4.1 30B Instruct is the largest Granite 4.1 language model: a dense, decoder-only transformer with 28.9B parameters across 64 layers (grouped-query attention, RoPE, SwiGLU), a 131,072-token context window, and Apache 2.0 licensing, released April 29, 2026. Like its siblings it was pretrained on roughly 15T tokens; the 30B's staged long-context extension ran out to 512K tokens during training, though the shipped configuration is 128K. Post-training used supervised fine-tuning on ~4.1M curated samples plus on-policy GRPO reinforcement learning. It posts the family's best scores: 80.16 MMLU, 64.09 MMLU-Pro, 89.65 IFEval, 71.02 ArenaHard, 94.16 GSM8K, 88.41 HumanEval, 73.68 BFCL v3, 45.76 GPQA. IBM ships FP8 variants optimized for vLLM and lists llama.cpp and SGLang support at launch, with availability on Ollama and LM Studio.
Positioning
A dense 30B is a contrarian choice in 2026, so be clear-eyed about what it buys. Unlike MoE peers such as Qwen3-30B-A3B, all 28.9B parameters fire on every token: you get simpler fine-tuning, predictable latency, and no expert-routing quirks, but you pay full memory bandwidth for every token generated — a low-active-parameter MoE will generate far faster on the same hardware. That is why we class it as workstation gear: plan on a 24GB GPU for 4-bit quants, or a higher-memory Apple Silicon Mac where bandwidth becomes the ceiling. IBM's FP8 build targets vLLM if you have datacenter cards.
What the memory buys: the family's best knowledge and tool use — 80.16 MMLU, 73.68 BFCL v3, 45.76 GPQA — and the best long-context retention (RULER 76.7 at 128K for the base model). What it doesn't: chat quality barely moves (71.02 ArenaHard versus 68.98 for the 8B), and there is still no reasoning mode.
Run it where tool-call accuracy and multilingual knowledge justify the footprint — agent and RAG backends on a 3090/4090-class box, under a license your legal team won't argue with. Skip it if you are throughput-bound or just chatting: the 8B delivers most of the practical value at a third of the memory, which is why it, not this model, is the family's star.
Overview
Granite 4.1 30B Instruct is the largest Granite 4.1 language model: a dense, decoder-only transformer with 28.9B parameters across 64 layers (grouped-query attention, RoPE, SwiGLU), a 131,072-token context window, and Apache 2.0 licensing, released April 29, 2026. Like its siblings it was pretrained on roughly 15T tokens; the 30B's staged long-context extension ran out to 512K tokens during training, though the shipped configuration is 128K. Post-training used supervised fine-tuning on ~4.1M curated samples plus on-policy GRPO reinforcement learning. It posts the family's best scores: 80.16 MMLU, 64.09 MMLU-Pro, 89.65 IFEval, 71.02 ArenaHard, 94.16 GSM8K, 88.41 HumanEval, 73.68 BFCL v3, 45.76 GPQA. IBM ships FP8 variants optimized for vLLM and lists llama.cpp and SGLang support at launch, with availability on Ollama and LM Studio.
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
Weaknesses
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 |
|---|
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 4.1 30B Instruct.
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
Can I use Granite 4.1 30B Instruct commercially?
What's the context length of Granite 4.1 30B Instruct?
Source: huggingface.co/ibm-granite/granite-4.1-30b
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Granite 4.1 30B Instruct runs on your specific hardware before committing money.