NVIDIA’s AceReason-Nemotron-14B is a powerful open-source model designed specifically for solving complex math and code problems with clarity and structure. What sets this model apart is its training approach — it’s built entirely through reinforcement learning, not just on general language, but on dedicated reasoning tasks.
Starting from the distilled DeepSeek-R1-Qwen foundation, AceReason was trained in two clear stages: first on math-only problems to sharpen its calculation and symbolic reasoning, and then on code-only tasks to strengthen its problem-solving and logic handling. This two-stage refinement pays off — the model consistently outperforms many larger models on tough benchmarks like AIME and LiveCodeBench.
Whether you’re testing math olympiad problems, writing recursive functions, or debugging algorithm logic, AceReason-Nemotron delivers thoughtful, structured answers that are often boxed with a final conclusion — just like a math student showing their work. It’s built for developers, researchers, and technical learners who want accuracy, depth, and structure in reasoning-heavy tasks.
Benchmarks
| Model | AIME 2024
(avg@64) | AIME 2025
(avg@64) | LCB v5
(avg@8) | LCB v6
(avg@8) |
|---|
| QwQ-32B | 79.5 | 65.8 | 63.4 | – |
| DeepSeek-R1-671B | 79.8 | 70.0 | 65.9 | – |
| Llama-Nemotron-Ultra-253B | 80.8 | 72.5 | 66.3 | – |
| o3-mini (medium) | 79.6 | 76.7 | 67.4 | – |
| Light-R1-14B | 74 | 60.2 | 57.9 | 51.5 |
| DeepCoder-14B (32K Inference) | 71 | 56.1 | 57.9 | 50.4 |
| OpenMath-Nemotron-14B | 76.3 | 63.0 | – | – |
| OpenCodeReasoning-Nemotron-14B | – | – | 59.4 | 54.1 |
| Llama-Nemotron-Super-49B-v1 | 67.5 | 60.0 | 45.5 | – |
| DeepSeek-R1-Distilled-Qwen-14B | 69.7 | 50.2 | 53.1 | 47.9 |
| DeepSeek-R1-Distilled-Qwen-32B | 72.6 | 54.9 | 57.2 | – |
| AceReason-Nemotron-14B 🤗 | 78.6 | 67.4 | 61.1 | 54.9 |
Recommended GPU Configurations for AceReason-Nemotron-14B
| GPU | VRAM | vCPUs | RAM | Storage | Max Tokens | Inference Speed | Notes |
|---|
| H100 SXM | 80 GB | 128–256 | 128–512 GB | 200+ GB | 32,768 | 🚀 Fastest | Ideal for full-context + batch runs |
| A100 80GB | 80 GB | 96–128 | 128–256 GB | 150+ GB | 32,768 | ⚡ Excellent | Reliable long-context inference |
| A6000 | 48 GB | 48–64 | 96–128 GB | 100+ GB | ~16,000* | ⚠️ Medium | Limited by VRAM for full 32K context |
| RTX 4090 | 24 GB | 32–48 | 64–96 GB | 100+ GB | ~8,000–12,000 | ⚠️ Slow | Only suitable for trimmed prompts |
Notes
- Use
--n_ctx 32768 flag with H100 or A100 80GB for full context reasoning.
- Lower VRAM GPUs can still run AceReason but will need
--n_ctx reduced (e.g., 8192).
- Ideal setups pair with
vllm loader for optimal memory streaming + performance.
Step-by-Step Process to Install NVIDIA AceReason-Nemotron-14B Locally
For the purpose of this tutorial, we will use a GPU-powered Virtual Machine offered by NodeShift; however, you can replicate the same steps with any other cloud provider of your choice. NodeShift provides the most affordable Virtual Machines at a scale that meets GDPR, SOC2, and ISO27001 requirements.
Step 1: Sign Up and Set Up a NodeShift Cloud Account
Visit the NodeShift Platform and create an account. Once you’ve signed up, log into your account.
Follow the account setup process and provide the necessary details and information.
Step 2: Create a GPU Node (Virtual Machine)
GPU Nodes are NodeShift’s GPU Virtual Machines, on-demand resources equipped with diverse GPUs ranging from H100s to A100s. These GPU-powered VMs provide enhanced environmental control, allowing configuration adjustments for GPUs, CPUs, RAM, and Storage based on specific requirements.
Navigate to the menu on the left side. Select the GPU Nodes option, create a GPU Node in the Dashboard, click the Create GPU Node button, and create your first Virtual Machine deploy
Step 3: Select a Model, Region, and Storage
In the “GPU Nodes” tab, select a GPU Model and Storage according to your needs and the geographical region where you want to launch your model.
We will use 1 x H100 GPU for this tutorial to achieve the fastest performance. However, you can choose a more affordable GPU with less VRAM if that better suits your requirements.
Step 4: Select Authentication Method
There are two authentication methods available: Password and SSH Key. SSH keys are a more secure option. To create them, please refer to our official documentation.
Step 5: Choose an Image
Next, you will need to choose an image for your Virtual Machine. We will deploy NVIDIA AceReason-Nemotron-14B on an NVIDIA Cuda Virtual Machine. This proprietary, closed-source parallel computing platform will allow you to install NVIDIA AceReason-Nemotron-14B on your GPU Node.
After choosing the image, click the ‘Create’ button, and your Virtual Machine will be deployed.
Step 6: Virtual Machine Successfully Deployed
You will get visual confirmation that your node is up and running.
Step 7: Connect to GPUs using SSH
NodeShift GPUs can be connected to and controlled through a terminal using the SSH key provided during GPU creation.
Once your GPU Node deployment is successfully created and has reached the ‘RUNNING’ status, you can navigate to the page of your GPU Deployment Instance. Then, click the ‘Connect’ button in the top right corner.
Now open your terminal and paste the proxy SSH IP or direct SSH IP.
Next, if you want to check the GPU details, run the command below:
nvidia-smi
Step 8: Check the Available Python version and Install the new version
Run the following commands to check the available Python version.
If you check the version of the python, system has Python 3.8.1 available by default. To install a higher version of Python, you’ll need to use the deadsnakes PPA.
Run the following commands to add the deadsnakes PPA:
sudo apt update
sudo apt install -y software-properties-common
sudo add-apt-repository -y ppa:deadsnakes/ppa
sudo apt update
Step 9: Install Python 3.11
Now, run the following command to install Python 3.11 or another desired version:
sudo apt install -y python3.11 python3.11-venv python3.11-dev
Step 10: Update the Default Python3 Version
Now, run the following command to link the new Python version as the default python3:
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 2
sudo update-alternatives --config python3
Then, run the following command to verify that the new Python version is active:
python3 --version
Step 11: Install and Update Pip
Run the following command to install and update the pip:
curl -O https://bootstrap.pypa.io/get-pip.py
python3.11 get-pip.py
Then, run the following command to check the version of pip:
pip --version
Step 12: Clone the WebUI Repo
Run the following command to clone the webui repo:
git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
Step 13: Run the One-Click Installer Script
Execute the following command to run the one-click installer script:
bash start_linux.sh
- It will automatically detect your GPU, install everything needed (Python, pip, CUDA toolkits, etc.)
- You’ll be prompted to select your GPU backend (choose
CUDA or NVIDIA GPU).
- Wait for it to finish setting up Python environment + dependencies.
Since our VM uses NVIDIA CUDA GPUs (e.g., A100, H100, A6000), choose:
A
Just type A and hit Enter.
What Happens Next
Once you select option A, the script will:
- Install
torch, vllm, and GPU-specific dependencies
- Prepare the web UI environment
- Prompt you to select or download a model (or you can do that manually)
- Launch the server on
http://127.0.0.1:7860
Step 14: SSH Port Forward
On your machine run the following command for SSH port forward:
ssh -L 7860:127.0.0.1:7860 root@<your_vm_ip>
Then open: http://localhost:7860 in your browser.
Step 15: Download the Model
Run the following command to download the model:
python3 download-model.py nvidia/AceReason-Nemotron-14B`
Step 16: Go back to the Web UI in browser
- Go to the “Model” tab
- Find
nvidia_AceReason-Nemotron-14B
- Click Load
Step 17: Test with a Math Prompt
Paste this into the chat:
Please reason step by step and put your final answer within \boxed{}:
If a number is multiplied by 4 and then increased by 5, the result is 29. What is the number?
Click Generate — and you’re good to go!
Conclusion
AceReason-Nemotron-14B stands out as a sharp and focused model purpose-built for problem-solving. Whether you’re working through competition-level math problems or writing structured code, it delivers step-by-step logic in a format that’s clear, methodical, and easy to follow.
Thanks to its reinforcement learning training on real reasoning tasks—and its compatibility with long 32K token prompts—AceReason doesn’t just guess; it thinks. With the right GPU setup, you get a tool that feels more like a thoughtful assistant than a language generator.
For researchers, developers, and learners who care about logic, structure, and precision — this is a model worth running.
