--- title: "Nvidia Nemotron: What the Open Model Did and Did Not Prove" url: https://syntaxradar.com/radar/how-nvidias-nemotron-outsmarted-gpt4-secret-sauce-ai-tuning author: "Abdul Rehman, Lead Systems Architect and Technical Director" publishedAt: 2026-10-25T09:14:00.000Z dateModified: 2026-10-29T10:33:58.900Z category: "#LLM-MODELS" topic: "nvidia nemotron" primarySource: https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF summary: "Nvidia Nemotron beat GPT-4o on three chat benchmarks in 2024. See how the reward-model tuning worked, the scores side by side, and what the tests miss." --- # Nvidia Nemotron: What the Open Model Did and Did Not Prove **Short answer:** In October 2024 Nvidia released Llama-3.1-Nemotron-70B-Instruct, a Llama 3.1 model retuned with a reward model. Its model card reports 85.0 on Arena Hard, ahead of GPT-4o (79.3) and Claude 3.5 Sonnet (79.2), with caveats. **Takeaway:** A reward model tuned a mid-sized open model to top three AI-judged chat benchmarks, though knowledge benchmarks barely moved. **Key figures (as reported by the source):** - Arena Hard score (model card): 85.0, vs GPT-4o 79.3 - AlpacaEval 2 LC (model card): 57.6, vs GPT-4o 57.5 - RewardBench score (paper): 94.1 Nvidia Nemotron is a family of open AI models from Nvidia, and the one that made headlines in October 2024 was a retuned version of Meta's Llama 3.1 70B. On three chat-quality tests, it scored above OpenAI's GPT-4o and Anthropic's Claude 3.5 Sonnet. The gain came from better training feedback applied to the same 70B model. The tests measure how much a judge prefers a model's answers in open-ended chat, and Nvidia's own model card says the model was not tuned for specialized areas such as math. ### What is Nemotron? Nemotron is Nvidia's name for its own line of language models and the tools used to train them. The model in the 2024 headlines is Llama-3.1-Nemotron-70B-Instruct. Its model card on Hugging Face (a site where AI developers share models) says the starting point was Meta's Llama-3.1-70B-Instruct. The "70B" means roughly 70 billion adjustable settings, called parameters. Nvidia took an existing open model and ran an extra round of training on top. The model card says the license is the NVIDIA Open Model License, with the Llama 3.1 Community License also applying, and the page says the model is "Built with Llama." The name now covers more than that one model. In December 2025 Nvidia [announced the Nemotron 3 family](https://nvidianews.nvidia.com/news/nvidia-debuts-nemotron-3-family-of-open-models), which comes in three sizes. The later sections return to that. ### What is a reward model, and how did it tune Nemotron? A reward model is a second AI that scores answers. It reads a question and a reply, then outputs a number for how good the reply is. Developers use those numbers as a training signal. The training method is called reinforcement learning from human feedback, or RLHF. The main model writes answers, the reward model scores them, and the main model is nudged toward answers that score higher. The model card says Nvidia used a specific variant called REINFORCE, run in Nvidia's NeMo Aligner software. The reward model itself was trained on human preference data. According to the [HelpSteer2-Preference paper](https://arxiv.org/abs/2410.01257), Nvidia released new preference labels for its HelpSteer2 dataset, where people marked which of two answers was better and wrote justifications. The paper compares two common ways of building a reward model and proposes a method that combines them. The paper reports that a Llama-3.1-70B reward model built this way scored 94.1 on RewardBench, a public test of reward models. The authors say that put it first among more than 140 reward models as of 1 October 2024. That score describes the judge, and the chat benchmarks below describe the model it trained. ![A researcher compares two printed answer sheets side by side at a desk in a quiet office](/uploads/blog/how-nvidias-nemotron-outsmarted-gpt4-secret-sauce-ai-tuning/gemini-supporting.jpg) ### How did Nemotron score against GPT-4o and Claude? The model card lists three automatic benchmarks, with figures dated 1 October 2024. Arena Hard and AlpacaEval 2 are tests where another AI model judges which answer is better. MT-Bench scores answers on a ten-point scale, also with an AI judge (GPT-4-Turbo here). According to the model card, the results were:
ModelArena HardAlpacaEval 2 LCMT-Bench
Llama-3.1-Nemotron-70B-Instruct85.057.68.98
GPT-4o (2024-05-13)79.357.58.74
Claude 3.5 Sonnet (20240620)79.252.48.81
Llama-3.1-405B-Instruct69.339.38.49
Llama-3.1-70B-Instruct (starting point)55.738.18.22
The jump from the starting model is large: Arena Hard rose from 55.7 to 85.0. Nemotron also beat Meta's own 405B model, which is roughly six times larger by parameter count. On AlpacaEval 2 the gap to GPT-4o is 0.1 points, which is inside the margin of error the card lists (a standard error of 1.65 for Nemotron and 1.47 for GPT-4o). Calling that a win overstates it. The model card also reports a Chatbot Arena rating of 1267 and a rank of 9 as of 24 October 2024, which places it near the top but below the number one slot. ### Did Nvidia Nemotron really beat GPT-4o? On those three tests, yes, by the model card's numbers. Judge-based chat benchmarks reward answers that an AI judge prefers. Researchers who built the length-controlled version of AlpacaEval wrote that existing open models had exploited its length bias, which is why the "LC" in the table exists. The Arena Hard authors likewise describe adjusting scores for style features such as length, markdown headers, bold text and lists. A model trained against a reward model can learn what the judges like. There was also a public question about contamination, meaning training on test data. Latent Space [looked into it](https://latent.space/p/ainews-legacy-24-10-16-ainews-did-nvidias-nemotron-70b-train-on-test) and reported that two knowledge benchmarks, GPQA and MMLU Pro, came in unchanged or worse compared with the base Llama 3.1 70B. It concluded the explanation was likely benign, with the gains coming from the new preference dataset and no training on test. The tuning made the model's chat answers much more to the liking of AI judges, while the two knowledge benchmarks stayed level or dropped. Nvidia's card describes the model as a demonstration of techniques for improving helpfulness in general instruction following. ![A server technician in a data center aisle checks a rack of GPU servers](/uploads/blog/how-nvidias-nemotron-outsmarted-gpt4-secret-sauce-ai-tuning/gemini-supporting.jpg) ### Can you run Nemotron yourself? The weights are public, so developers can download them. The practical cost is hardware. The model card says the model needs two or more 80GB GPUs and at least 150GB of free disk space. Output is capped at 4,000 tokens (a token is a word fragment, roughly three quarters of a word in English). The card also warns that the Hugging Face version has been converted from the NeMo Aligner version, so results may differ slightly from the published scores. For most people, using the model through a hosted service is easier than running it. How those services price open models is covered in our look at [LLM API pricing cuts](/radar/llm-api-pricing-cuts-2026). Open here means the weights can be downloaded under a license with conditions. Our explainer on [open-weight models](/radar/open-weight-models-explained-ibm) covers how that differs from fully open source. ### What are Nemotron 3 Nano, Super and Ultra? Since the 2024 model, Nvidia has moved to a new generation. Its December 15, 2025 announcement describes Nemotron 3 in three sizes, using figures from Nvidia's release. Nano has 30 billion total parameters with up to 3 billion active at a time. Super has about 100 billion total with up to 10 billion active per token. Ultra has about 500 billion total with up to 50 billion active per token. "Active" parameters are the share of the model used for any single piece of text, a design that keeps running costs down. At launch only Nano was available, on Hugging Face and through providers including Baseten, DeepInfra, Fireworks, FriendliAI, OpenRouter and Together AI. Nvidia said Super and Ultra were expected in the first half of 2026. Nvidia's model pages are the place to check whether those two have shipped. Nvidia says Nano delivers up to 4x higher token throughput than Nemotron 2 Nano and cuts reasoning-token generation by up to 60%. The release also says Nvidia is publishing three trillion tokens of training datasets and open-source training libraries (NeMo Gym and NeMo RL), though it does not name a license in that announcement. ### What to watch next The 2024 Nemotron result matters mostly as a method. A strong reward model trained on carefully labeled human preferences can move a mid-sized open model a long way on judge-scored chat tests. Nvidia published the preference data and a paper, so others can test the claim. Independent evaluations that control for length and style will show whether the same gap to GPT-4o holds. The Nemotron 3 Super and Ultra releases, and how they score on knowledge and reasoning tests, will show whether the method carries over to the new generation. Nvidia's decision to keep publishing training data matters because that is what lets outsiders check the work. ## Frequently asked questions ### What is Nvidia Nemotron? Nvidia Nemotron is Nvidia's line of open language models and training tools. The 2024 headline model, Llama-3.1-Nemotron-70B-Instruct, is Meta's Llama 3.1 70B retrained with reinforcement learning from human feedback. In December 2025 Nvidia announced Nemotron 3, a newer family in Nano, Super and Ultra sizes that it also calls open. ### Did Nemotron really beat GPT-4o? On three AI-judged chat benchmarks, the model card reports it did: Arena Hard 85.0 against 79.3, and MT-Bench 8.98 against 8.74. On AlpacaEval 2 the two are 57.6 and 57.5, within the margin of error. The model card says it was not tuned for specialized domains such as math, so the wins are narrow. ### What is a reward model in AI? A reward model is an AI that scores another model's answers. Developers train it on human judgments of which answers are better, then use its scores as a training signal in reinforcement learning from human feedback. The main model is adjusted over many rounds toward replies the reward model rates higher, which is how Nvidia tuned its Llama-based model. ### Can I run Nvidia Nemotron for free? The 70B model's weights can be downloaded under the NVIDIA Open Model License and the Llama 3.1 Community License, so there is no per-token fee when you host it yourself. The model card says it needs two or more 80GB GPUs and at least 150GB of disk space, so the hardware is the real cost for most people. ## Source Based on Nvidia's model card and paper. https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF --- Published on SyntaxRadar (https://syntaxradar.com). Canonical URL: https://syntaxradar.com/radar/how-nvidias-nemotron-outsmarted-gpt4-secret-sauce-ai-tuning. Last modified 2026-10-29T10:33:58.900Z.