Granite 4.1 30B Instruct
By IBM · United States
Updated 2026-07-13
Overview
IBM's dense 30B Granite 4.1: Apache 2.0, 12 languages, 131k context, with OpenAI-compatible tool calling. Built on the same GB200 NVL72 cluster as the rest of the 4.1 lineup.
When to pick this model
- Enterprise agents requiring OpenAI-compatible function calling
- Apache 2.0 deployments where Granite 8B isn't enough
- Multilingual products across 12 languages including French
- Long-context workflows up to 131k
- Single-GPU production on RTX 5090 or A100 class hardware
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 17 GB |
| Q5_K_M | 21 GB |
| Q8_0 | 32 GB |
| FP16 (no quantization) | 60 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Granite 4.1 30B Instruct wants a 24 GB card at Q4_K_M (17 GB). Stepping up to Q8_0 nearly doubles the footprint to 32 GB, and unquantized FP16 weights take 60 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Granite 4.1 30B Instruct needs roughly 36 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 3 tokens/sec on entry-level GPUs, on the order of 12 tokens/sec on a mid-range card, and up to 30 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Granite 4.1 30B Instruct to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Granite 4.1 30B Instruct |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 17 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 17 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 17 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (21 GB used) |
| 32 GB | RTX 5090 | Q8_0 (32 GB used) |
Which GPU should you buy to run Granite 4.1 30B Instruct?
To run Granite 4.1 30B Instruct locally at Q4, you need ~17 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Strengths
- Apache 2.0 with IBM-grade transparency
- Native OpenAI function-calling schema
- 12 languages including French
- 131k context window
- Official Ollama tag with multiple quantizations
Limitations
- Needs ~32 GB VRAM at Q4 — RTX 5090 territory
- No MoE variant at this size
- Non-English reasoning trails English
Typical workloads
In our catalog grid, Granite 4.1 30B Instruct is filed under Enterprise Tool Calling, Long Context RAG, Production Agents — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); multilingual workloads.
The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense Transformer · 64 layers · GQA 32Q/8KV · embedding 4096 · MLP hidden 32,768 · SwiGLU · RoPE · RMSNorm
Training: Fine-tuned from Granite-4.1-30B-Base. SFT + RL alignment pipeline. 12 languages: EN, DE, ES, FR, JA, PT, AR, CS, IT, KO, NL, ZH. NVIDIA GB200 NVL72 cluster (CoreWeave).
The Granite to pick when 8B feels light: Apache 2.0, function-calling native, and built for enterprise.
Quick start
ollama run granite4.1:30bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Granite 4.1 30B Instruct need?
At the recommended Q4_K_M quantization, Granite 4.1 30B Instruct needs about 17 GB of VRAM. Q8_0 takes 32 GB, and unquantized FP16 weights take 60 GB.
Can Granite 4.1 30B Instruct run without a GPU?
Yes — with roughly 36 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Granite 4.1 30B Instruct support?
Granite 4.1 30B Instruct supports a 128k-token context window (131,072 tokens).
Can I use Granite 4.1 30B Instruct commercially?
Yes. Granite 4.1 30B Instruct is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Granite 4.1 30B Instruct on consumer hardware?
Our compatibility engine estimates on the order of 12 tokens/sec on a mid-range GPU and up to 30 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Granite 4.1 30B Instruct should I download first?
Start with Q4_K_M (17 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.