[whatmodel.sh](../../README.md) / AceReason-Nemotron-1.0-14B # AceReason-Nemotron-1.0-14B NVIDIA · released 2025-05 · 14.77B params · nvidia-open-model-license **score 50.1** ±1.8 · #370 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 AceReason-Nemotron-1.0-14B among every model on that task or test, worst to best. 
task    vs the others         rank  counts  tests
coding  ▁▁▁▃▅●█▇▅▄▃▁   #301 of 469     99%      3
coding · #301 of 469 · 99% of its score Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3-VL 30B-A3B Instruct](qwen3-vl-30b-a3b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Yi 1.5 9B Chat](yi-1-5-9b-chat.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty            counts  why
LiveCodeBench Pro 2025Q2 (Easy) (…   47.9%  ▄·●····▄▄▄█▄ beats 1/7    medium · avg 46%         44%  at its level
LiveCodeBench Pro 2025Q2 (Medium)…    2.2%  ▃●····▃·█·▃▃ beats 1/7    very hard · avg 2.6%     35%  sharp · too hard for it
LiveCodeBench v6 (Aug 2024–May 20…   58.7%  ▄●···▄·▄▄█·▄ beats 1/7    medium · avg 58%         20%  at its level
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md) 
source                               results  share of the score
NVIDIA (submitted / maker-reported)        3   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                   1% ▇
weighted average                              = 50.1
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 AceReason-Nemotron-1.0-14B (Q4_K_M, needs ~8.99 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-14B-GGUF/tree/defcea7703e6ca24360559ead83fd075c7bf0d8b)) ```sh llama cli -hf bartowski/nvidia_AceReason-Nemotron-14B-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_AceReason-Nemotron-14B-GGUF:Q4_K_M ``` Other versions: Q8_0 (~15.7 GB) `llama cli -hf bartowski/nvidia_AceReason-Nemotron-14B-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 AceReason-Nemotron-1.0-14B (Q4_K_M, needs ~8.99 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-14B-GGUF/tree/defcea7703e6ca24360559ead83fd075c7bf0d8b)) ```sh lms get https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-14B-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 (~15.7 GB) `lms get https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-14B-GGUF@Q8_0 -y` · 4bit (~8.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/AceReason-Nemotron-14B-4bit -y` · 8bit (~15.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/AceReason-Nemotron-14B-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 AceReason-Nemotron-1.0-14B (Q4_K_M, needs ~8.99 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-14B-GGUF/tree/defcea7703e6ca24360559ead83fd075c7bf0d8b)) ```sh ollama run hf.co/bartowski/nvidia_AceReason-Nemotron-14B-GGUF:Q4_K_M ``` Other versions: Q8_0 (~15.7 GB) `ollama run hf.co/bartowski/nvidia_AceReason-Nemotron-14B-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 AceReason-Nemotron-1.0-14B (4bit, needs ~8.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/AceReason-Nemotron-14B-4bit/tree/8a00fd6e8500be3b93990984b744f81589aa6ccf)) ```sh mlx_lm.chat --model mlx-community/AceReason-Nemotron-14B-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/AceReason-Nemotron-14B-4bit ``` Other versions: 8bit (~15.7 GB, Mac only) `mlx_lm.chat --model mlx-community/AceReason-Nemotron-14B-8bit` Model repository and documentation: [huggingface.co/nvidia](https://huggingface.co/nvidia/AceReason-Nemotron-14B) [interactive version](https://whatmodel.sh/model/acereason-nemotron-14b) · [JSON](https://whatmodel.sh/api?format=json&all=1)