[whatmodel.sh](../../README.md) / Granite 3.2 8B Instruct
# Granite 3.2 8B Instruct
IBM · released 2025-02 · 8.17B params · apache-2.0
**score 45.1** ±1.2 · #476 of 732 · from 11 benchmark results across 3 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Granite 3.2 8B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃●█▄▅▅▄▄▂▁ #272 of 313* 5.7% 1
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #278 of 402* 12% 2
math ▁▁▃▅●▆▄█▄▂▁▁ #307 of 431 41% 3
agent tasks · #272 of 313 · 5.7% of its score
Beats [DeepSeek R1 0528 Qwen3 8B](deepseek-r1-0528-qwen3-8b.md), [Magistral Small 1.0 (2506)](magistral-small-2506.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank higher overall. Trails [Granite 4.0 H 1B](granite-4-0-h-1b.md), [Granite 4.0 1B](granite-4-0-1b.md), [SmolLM3 3B](smollm3-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL V4 (December 2025 archive) 26.9% ▁▁▁▂●█▅▅▆▂▂▂ beats 18/77 hard · avg 31% 5.7% typical
chat & writing · #278 of 402 · 12% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Ministral 3 14B](ministral-3-14b.md), [Ministral 3 8B](ministral-3-8b.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [LFM2 1.2B](lfm2-1-2b.md), [North Micro Vision Instruct (2.4B)](north-micro-vision-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena-Hard (version/judge unspeci… 55.3% █·▄··▄·····● beats 4/5 medium · avg 55% 6.3% at its level
IFEval (aggregation unspecified) … 74.3% ▄▄·▄······▄● beats 4/5 easy · avg 78% 5.8% at its level
math · #307 of 431 · 41% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [Llama 3.1 Nemotron 70B Instruct](llama-3-1-nemotron-70b.md), [GPT-4 (0613)](gpt-4-0613.md) here, though they rank higher overall. Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Gemma 7B (base)](gemma-7b-base.md), [Mistral 7B v0.1 (base)](mistral-7b-v0-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 81.7% █··▄·····▄·● beats 5/5 very easy · avg 89% 21% sharp
AIME 2024 (maker) 2.4% █···▄●▄····▄ beats 3/5 very hard · avg 8.9% 12% sharp · too hard for it
MATH-500 (maker) 52.8% █···▄·●▄···▄ beats 3/5 easy · avg 70% 7.2% at its level
other tests (5)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 71.9% █·▄·····▄▄·● beats 5/5 easy · avg 81% 17% sharp · at its level
MMLU (maker) 66.8% ▄█········▄● beats 5/5 easy · avg 76% 15% at its level
PopQA (maker) 28.0% ▄·█······▄·● beats 4/5 hard · avg 33% 6.4% at its level
Vectara hallucination leaderboard… 91.3% ▁▁▂▃●▄█▆▃▂▂▁ beats 29/140 very easy · avg 94% 1.3% blunt · noisy
DROP (maker) 58.3% ▃▃·····▃···● beats 3/5 medium · avg 54% 0.7% blunt · at its level
source results share of the score
ibm-granite/granite-3.3-8b-instruct mod… 9 92% ▇▇▇▇▇▇▇▇▇▇▇
Berkeley Function Calling Leaderboard r… 1 6% ▇
Vectara hallucination leaderboard 1 1% ▇
prior: pull toward the middle 1% ▇
weighted average = 45.1
Licences: ibm-granite/granite-3.3-8b-instruct model card ([Apache-2.0](../../licenses/ibm-granite--granite-3.3-8b-instruct.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Granite 3.2 8B Instruct (Q4_K_M, needs ~4.9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.2-8b-instruct-GGUF/tree/c18af5bc7139c800d2bcd2ceb0386ddbc42d0dca))
```sh
llama cli -hf lmstudio-community/granite-3.2-8b-instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/granite-3.2-8b-instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.7 GB) `llama cli -hf lmstudio-community/granite-3.2-8b-instruct-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Granite 3.2 8B Instruct (Q4_K_M, needs ~4.9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.2-8b-instruct-GGUF/tree/c18af5bc7139c800d2bcd2ceb0386ddbc42d0dca))
```sh
lms get https://huggingface.co/lmstudio-community/granite-3.2-8b-instruct-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~8.7 GB) `lms get https://huggingface.co/lmstudio-community/granite-3.2-8b-instruct-GGUF@Q8_0 -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Granite 3.2 8B Instruct (Q4_K_M, needs ~5.4 GB, from [ollama.com](https://ollama.com/library/granite3.2/tags))
```sh
ollama run granite3.2:8b-instruct-q4_K_M
```
Other versions: Q8_0 (~10.3 GB) `ollama run granite3.2:8b-instruct-q8_0`
Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3.2-8b-instruct)
[interactive version](https://whatmodel.sh/model/granite-3-2-8b) · [JSON](https://whatmodel.sh/api?format=json&all=1)