whatmodel.sh / Granite 3.1 1B-A400M Instruct

Granite 3.1 1B-A400M Instruct

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Granite 3.1 1B-A400M Instruct is an open-weights language model from IBM, released December 2024. Its published size is 1.3B parameters (0.4B active per token) and a 131k-token context window. Licence: apache-2.0.

Catalogue identity notes

IBM Granite-3.1-1B-A400M-Instruct (ibm-granite/granite-3.1-1b-a400m-instruct @0da7a48, created 2024-12-06, apache-2.0, 1,334,628,352 params; card: '# Parameters 1.3B', '# Active parameters 400M'). Lab rows from the Granite 3.1 MoE card tables with the catalogued 8B/2B siblings.

Model information sources

Release date ↗ · Weights licence ↗

Card architecture table '# Parameters 1.3B' / '# Active parameters 400M' (HF safetensors metadata 1,334,628,352).

28.8±1.8#652 of 719 LLMsfrom 6 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of Granite 3.1 1B-A400M Instruct, out of 828 with a score, then more from IBM.

#modelscorewhereRAMreleased
749Gemma 1.1 2B IT Google29±1local~3.2GB2024-04
750StableLM-Base-Alpha 7B v2 Stability AI29±1local~30GB2023-08
751Granite 3.1 1B-A400M Instruct IBM29±2local~2.3GB2024-12
752OPT-30B Meta29±1local~64GB2022-05
753NexusRaven V2 13B Nexusflow29±3local~9.3GB2023-12
more from IBM
192Granite 4.2 30B IBM63±1api $0.28/M +local~19GB2026-08
239Granite 4.2 8B IBM60±1api $0.11/M +local~6.6GB2026-08
276Granite 4.2 3B IBM57±1api $0.052/M +local~3.7GB2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #347 of 393 * · 8.5% of its score · 1 test

Beats Gemma 4 E2B, Code Llama 70B Instruct, Phi-3-mini-4k-instruct (June 2024 update) here, though they rank higher overall. Trails Gemma 3 270M IT, SmolLM2 360M Instruct, Granite 4.0 H 350M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
IFEval (aggregation unspecified) lab46.9% ✎▁▃▃▁▂▄▄▆█▃▂▁ beats 6/141easy · avg 78%▇▇▇ 8.5%at its level
math▁▃▃▅▆▅▄█▄▁▁▁ #380 of 398 * · 29% of its score · 1 test

Beats Command R (03-2024), Molmo 7B-D (0924), Mistral 7B Instruct v0.3 here, though they rank higher overall.

testresultvs the others in its rundifficultycountswhy
GSM8K lab33.9% ✎▂▂▂▅▅▆▅▇█▇▄▃ beats 25/146very easy · avg 85%▇▇▇▇▇▇▇▇▇▇▇▇ 29%sharp · at its level
other tests4 not in a task · 61% of its score
testresultvs the others in its rundifficultycountswhy
ARC Challenge lab42.7% ✎▆▁▁▂▂▅▇█▄▃▃▂ beats 45/97easy · avg 82%▇▇▇▇▇▇▇▇▇ 23%sharp
WinoGrande lab62.4% ✎▁···▂▁▁▅█▇▃▁ beats 31/87easy · avg 84%▇▇▇▇▇▇ 16%typical
HellaSwag lab66.0% ✎▁▁▁▁▂▂▃▇█▆▅▃ beats 32/85very easy · avg 89%▇▇▇▇▇▇ 15%at its level
MMLU lab26.1% ✎▃▁▁▁▁▂▃▅█▃▆▃ beats 1/183easy · avg 75%▇▇▇ 7.4%typical

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Granite 3.1 1B-A400M Instruct · needs ~0.8 GB · from huggingface.co/lmstudio-community
    chat in the terminal
    llama cli -hf lmstudio-community/granite-3.1-1b-a400m-instruct-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf lmstudio-community/granite-3.1-1b-a400m-instruct-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Granite 3.1 1B-A400M Instruct · needs ~0.8 GB · from huggingface.co/lmstudio-community

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/lmstudio-community/granite-3.1-1b-a400m-instruct-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Granite 3.1 1B-A400M Instruct · needs ~1.4 GB · from ollama.com
    chat in the terminal
    ollama run granite3.1-moe:1b

Weights and documentation: huggingface.co/ibm-granite ↗

$ how the score was worked out · ibm-granite/granite-3.1-3b-a8… 99%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
ibm-granite/granite-3.1-3b-a800m-instruct model card · lab-reported6▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 28.8

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json