[whatmodel.sh](../../README.md) / OpenReasoning-Nemotron-32B
# OpenReasoning-Nemotron-32B
NVIDIA · released 2025-07 · 32.76B params · cc-by-4.0
**score 58.7** ±1.5 · #216 of 732 · from 3 benchmark results across 1 source · † no independently run results verified
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks OpenReasoning-Nemotron-32B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇█●▅▄▃▁ #187 of 469 99% 3
coding · #187 of 469 · 99% of its score
Beats [Qwen3.5 9B](qwen3-5-9b.md), [Gemini 2.5 Flash](gemini-2-5-flash.md), [Devstral 2 (2512)](devstral-2.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 8B Reasoning 2512](ministral-3-8b-reasoning.md), [OpenThinker 32B](openthinker-32b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench Pro 2025Q2 (Medium)… 8.3% ▃▃····▃·█·●▃ beats 6/7 very hard · avg 2.6% 61% sharp · too hard for it
LiveCodeBench Pro 2025Q2 (Easy) (… 66.2% ▄·▄····▄▄▄●▄ beats 6/7 medium · avg 46% 27% at its level
LiveCodeBench v6 (Aug 2024–May 20… 70.2% ▄▄···▄·▄●█·▄ beats 4/7 medium · avg 58% 12% at its level
source results share of the score
NVIDIA (submitted / maker-reported) 3 99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 1% ▇
weighted average = 58.7
Licences: NVIDIA ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## 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 OpenReasoning-Nemotron-32B (Q4_K_M, needs ~19.85 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-32B-GGUF/tree/5e5d5c072b86580ef2920e2f3915e6c5696eb65d))
```sh
llama cli -hf bartowski/nvidia_OpenReasoning-Nemotron-32B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/nvidia_OpenReasoning-Nemotron-32B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~34.82 GB) `llama cli -hf bartowski/nvidia_OpenReasoning-Nemotron-32B-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 OpenReasoning-Nemotron-32B (Q4_K_M, needs ~19.85 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-32B-GGUF/tree/5e5d5c072b86580ef2920e2f3915e6c5696eb65d))
```sh
lms get https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-32B-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 (~34.82 GB) `lms get https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-32B-GGUF@Q8_0 -y` · 4bit (~18.4 GB, Mac only) `lms get https://huggingface.co/litmudoc/OpenReasoning-Nemotron-32B-MLX-Q4 -y` · 8bit (~34.8 GB, Mac only) `lms get https://huggingface.co/litmudoc/OpenReasoning-Nemotron-32B-MLX-Q8 -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 OpenReasoning-Nemotron-32B (Q4_K_M, needs ~19.85 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-32B-GGUF/tree/5e5d5c072b86580ef2920e2f3915e6c5696eb65d))
```sh
ollama run hf.co/bartowski/nvidia_OpenReasoning-Nemotron-32B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~34.82 GB) `ollama run hf.co/bartowski/nvidia_OpenReasoning-Nemotron-32B-GGUF:Q8_0`
### 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 OpenReasoning-Nemotron-32B (4bit, needs ~18.4 GB, Mac only, from [huggingface.co/litmudoc](https://huggingface.co/litmudoc/OpenReasoning-Nemotron-32B-MLX-Q4/tree/b5729c305e444aaeebf8f70a24d77ca56bacd380))
```sh
mlx_lm.chat --model litmudoc/OpenReasoning-Nemotron-32B-MLX-Q4
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model litmudoc/OpenReasoning-Nemotron-32B-MLX-Q4
```
Other versions: 8bit (~34.8 GB, Mac only) `mlx_lm.chat --model litmudoc/OpenReasoning-Nemotron-32B-MLX-Q8`
Model repository and documentation: [huggingface.co/nvidia](https://huggingface.co/nvidia/OpenReasoning-Nemotron-32B)
[interactive version](https://whatmodel.sh/model/openreasoning-nemotron-32b) · [JSON](https://whatmodel.sh/api?format=json&all=1)