Family Granite · 8B parameters

Granite 4.1 8B Instruct

Dense 8B Apache 2.0, 12 languages including FR, 131k context, GQA 32Q/8KV. MMLU 73.84, HumanEval 85.37. Released April 29, 2026.

🇺🇸 IBM·License Apache 2.0·Context 128k tokens·Output April 29, 2026·Fits within the 128 GB of the GIGABYTE AI TOP ATOM← Catalog

01What it can do

Strengths
  • 100% free Apache 2.0
  • Stronger tool calling and instruction following
  • 12 native languages, including FR
  • 131k context
  • Excellent performance-to-size ratio
Limitations to know
  • —No official Ollama tag at release
  • —English remains stronger than French for reasoning
  • —No MoE variant in this release
Architecture
Dense Transformer · 40 layers · GQA 32Q/8KV · 4096 embedding · MLP hidden size 12,800 · RoPE
Training
Improved post-training: SFT + RL alignment. 12 languages: EN, DE, ES, FR, JA, PT, AR, CS, IT, KO, NL, ZH. NVIDIA GB200 NVL72 cluster (CoreWeave).
Ideal for
Enterprise tool callingMultilingual RAG8B coding assistant

05Install

Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.

$# HuggingFace : ibm-granite/granite-4.1-8b
⚠
First download: between 2 and 40 GB depending on the selected quantization. Plan for sufficient disk space; a stable connection is recommended. Subsequent launches are instant.

02Required memory

Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.

Q4_K_M
The lightest, ~5% loss
5 GB
Q5_K_M
Good quality/size compromise
6 GB
Q8_0
Nearly indistinguishable from FP16
9 GB
FP16
Full precision — server use
16 GB
Fallback CPU · If you don't have a GPU, allow 10 GB of RAM minimum to run this model at reduced speed.

What hardware do you need for Granite 4.1 8B Instruct?

To run Granite 4.1 8B Instruct locally with Q4 quantization, you need about 5 GB of VRAM. An option to compare: RTX 5060 Ti 16GB (ASUS Prime) — leave some headroom for the system and context; check engine compatibility with the GPU.

Current offer: RTX 5060 Ti 16GB (ASUS Prime)
AmazonSee price →

Affiliate links — commission possible at no extra cost to you; independent recommendations. As an Amazon Associate, BestLLMfor earns from qualifying purchases.

On the go: Granite 4.1 8B Instruct also runs on a RTX laptop PC (16 GB of VRAM) →

03Expected speed

Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.

Entry-level
~12t/s
GTX 1650, RX 6600, MBA M2 8GB
Mid-range
~35t/s
RTX 4060, 4070, MBP M3 Pro
High-end
~90t/s
RTX 4090, M4 Max, Radeon 7900

04Public benchmarks

Scores reproduced from model cards or MMLU-Pro / community sources. Unit: % correct answers.

MMLU
73.84
GSM8K
92.49
HumanEval
85.37
ArenaHard
68.98
AlpacaEval
50.08