[whatmodel.sh](../../README.md) / Granite 3.3 8B Instruct
# Granite 3.3 8B Instruct
IBM · released 2025-04 · 8.17B params · apache-2.0
**score 46.8** ±1.2 · #442 of 732 · from 13 benchmark results across 4 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Granite 3.3 8B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #272 of 402* 12% 2
math ▁▁▃▅█●▄█▄▂▁▁ #249 of 431 46% 5
chat & writing · #272 of 402 · 12% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Ministral 3 14B](ministral-3-14b.md), [OpenThinker2 32B](openthinker2-32b.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [North Micro Vision Instruct (2.4B)](north-micro-vision-instruct.md), [Granite 4.0 H 1B](granite-4-0-h-1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena-Hard (version/judge unspeci… 57.6% █·▄··▄·····● beats 5/5 medium · avg 55% 6.3% at its level
IFEval (aggregation unspecified) … 74.8% ▄▄·▄······▄● beats 5/5 easy · avg 78% 5.5% at its level
math · #249 of 431 · 46% of its score
Beats [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md), [Command A (03-2025)](command-a.md), [GPT-4o (2024-05-13)](gpt-4o-2024-05-13.md) here, though they rank higher overall. Trails [Gemma 7B (base)](gemma-7b-base.md), [CodeGemma 7B (v1.0)](codegemma-7b.md), [Phi-2 (2.7B)](phi-2.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 80.9% █··▄·····▄·● beats 4/5 very easy · avg 89% 18% sharp
AIME 2024 (maker) 8.1% █···▄▄▄····● beats 5/5 very hard · avg 8.9% 14% sharp · too hard for it
MATH-500 (maker) 69.0% █···▄·▄▄···● beats 5/5 easy · avg 70% 6.9% at its level
OMEGA (math generalisation) 10.7% █···●·▄·▄··█ beats 2/6 hard · avg 19% 3.3% typical
MATH (subset unspecified) 67.3% ██····●█·███ beats 2/6 easy · avg 70% 3.3% at its level · noisy
other tests (6)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 69.1% █·▄·····●▄·▄ beats 3/5 easy · avg 81% 16% sharp · at its level
MMLU (maker) 65.5% ▄█········●█ beats 3/5 easy · avg 76% 15% at its level
PopQA (maker) 26.2% ▄·█······●·█ beats 3/5 hard · avg 33% 6.7% at its level
DiagnosticIQ industrial maintenan… 59.5% ▁··▃▁▁▁●█▅▁▅ beats 8/24 hard · avg 51% 2.9% blunt · at its level
DROP (maker) 59.4% ▃▃·····▃···● beats 5/5 medium · avg 54% 0.7% blunt · at its level
Vectara hallucination leaderboard… 89.4% ▁▂▁▂▅●▂▃▄▃▁▁ beats 37/98 very easy · avg 90% 0.0% blunt
source results share of the score
ibm-granite/granite-3.3-8b-instruct mod… 9 90% ▇▇▇▇▇▇▇▇▇▇▇
allenai/Olmo-3-7B-Instruct model card 2 7% ▇
DiagnosticIQ: A Benchmark for LLM-Based… 1 3% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 1% ▇
weighted average = 46.8
Licences: ibm-granite/granite-3.3-8b-instruct model card ([Apache-2.0](../../licenses/ibm-granite--granite-3.3-8b-instruct.txt)) · allenai/Olmo-3-7B-Instruct model card ([Apache-2.0](../../licenses/allenai--Olmo-3-7B-Instruct.txt)) · DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · 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 3.3 8B Instruct (Q4_K_M, needs ~4.9 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3.3-8b-instruct-GGUF/tree/e40e9dd739c7be00fa965c16ce167088190ce114))
```sh
llama cli -hf ibm-granite/granite-3.3-8b-instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf ibm-granite/granite-3.3-8b-instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.7 GB) `llama cli -hf ibm-granite/granite-3.3-8b-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 3.3 8B Instruct (Q4_K_M, needs ~4.9 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3.3-8b-instruct-GGUF/tree/e40e9dd739c7be00fa965c16ce167088190ce114))
```sh
lms get https://huggingface.co/ibm-granite/granite-3.3-8b-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/ibm-granite/granite-3.3-8b-instruct-GGUF@Q8_0 -y` · 4bit (~4.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/granite-3.3-8b-instruct-4bit -y` · 8bit (~8.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/granite-3.3-8b-instruct-8bit -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 3.3 8B Instruct (Q4_K_M, needs ~5.4 GB, from [ollama.com](https://ollama.com/library/granite3.3/tags))
```sh
ollama run granite3.3:8b
```
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Granite 3.3 8B Instruct (4bit, needs ~4.6 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/granite-3.3-8b-instruct-4bit/tree/0751a30ba2420ecd5c2f142707dd4fcfacc4486e))
```sh
mlx_lm.chat --model mlx-community/granite-3.3-8b-instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/granite-3.3-8b-instruct-4bit
```
Other versions: 8bit (~8.7 GB, Mac only) `mlx_lm.chat --model mlx-community/granite-3.3-8b-instruct-8bit`
Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3.3-8b-instruct)
[interactive version](https://whatmodel.sh/model/granite-3-3-8b) · [JSON](https://whatmodel.sh/api?format=json&all=1)