--- title: Llama 3.1 Nemotron 70B Instruct — Top LLM | ModelsLab description: Access Llama 3.1 Nemotron 70B Instruct HF via API for helpful responses topping Arena Hard 85.0. Try this NVIDIA-tuned 70B model now. url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/llama-31-nemotron-70b-instruct-hf canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/llama-31-nemotron-70b-instruct-hf type: website component: Seo/ModelPage generated_at: 2026-05-13T09:44:01.365523Z --- Available now on ModelsLab · Language Model Llama 3.1 Nemotron 70B Instruct HF Helpful Responses Top Benchmarks --- [Try Llama 3.1 Nemotron 70B Instruct HF](/models/meta/nvidia-Llama-3.1-Nemotron-70B-Instruct-HF) [API Documentation](https://docs.modelslab.com) Deploy Nemotron 70B Now --- Arena Leader ### 85.0 Arena Hard Leads automatic alignment benchmarks over GPT-4o and Claude 3.5 Sonnet. 128K Context ### Process Long Inputs Handles 128k token context window for extended conversations and documents. RLHF Tuned ### NVIDIA Helpfulness Boost Fine-tuned with REINFORCE on Llama-3.1-70B-Instruct for precise user responses. Examples See what Llama 3.1 Nemotron 70B Instruct HF can create --- Copy any prompt below and try it yourself in the [playground](/models/meta/nvidia-Llama-3.1-Nemotron-70B-Instruct-HF). Code Review “Review this Python function for efficiency and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2)” Tech Summary “Summarize key advancements in transformer models since 2017, focusing on efficiency improvements and scaling laws.” Data Analysis “Analyze this dataset of sales figures by quarter and predict Q5 trend: Q1: 1200, Q2: 1500, Q3: 1800, Q4: 2100.” Architecture Design “Design a scalable microservices architecture for a cloud-based e-commerce platform handling 10k requests per second.” For Developers A few lines of code. Nemotron 70B. One API call. --- ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed. - **Serverless:** scales to zero, scales to millions - **Pay per token,** no minimums - **Python and JavaScript SDKs,** plus REST API [API Documentation ](https://docs.modelslab.com) PythonJavaScriptcURL Copy ```

import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

``` FAQ Common questions about Llama 3.1 Nemotron 70B Instruct HF --- [Read the docs ](https://docs.modelslab.com) ### What is Llama 3.1 Nemotron 70B Instruct HF? NVIDIA-customized 70B LLM from Llama-3.1-70B-Instruct base. Improves response helpfulness via RLHF. Tops Arena Hard at 85.0 as of Oct 2024. ### How to use Llama 3.1 Nemotron 70B Instruct HF API? Access via LLM endpoint with standard chat completions format. Supports 128k context. Integrate directly in code for inference. ### What are Llama 3.1 Nemotron 70B Instruct HF benchmarks? Arena Hard 85.0, AlpacaEval 2 LC 57.6, MT-Bench 8.98. Elo 1267 on Chatbot Arena, rank 9 as of Oct 2024. ### Is Llama 3.1 Nemotron 70B Instruct HF model fast? Outputs 44 tokens/second, TTFT 1.74s median. Below average speed but concise at 3.8M tokens on Intelligence Index. ### Llama 3.1 Nemotron 70B Instruct HF alternative options? Compare to Llama-3.1-70B-Instruct or quantized GGUF/AWQ versions. Use this API for hosted access without local setup. ### What context length for llama 3.1 nemotron 70b instruct hf? Supports 128k-130k tokens. Processes long histories and documents in one request. Ready to create? --- Start generating with Llama 3.1 Nemotron 70B Instruct HF on ModelsLab. [Try Llama 3.1 Nemotron 70B Instruct HF](/models/meta/nvidia-Llama-3.1-Nemotron-70B-Instruct-HF) [API Documentation](https://docs.modelslab.com) --- *This markdown version is optimized for AI agents and LLMs.* **Links:** - [Website](https://modelslab.com) - [API Documentation](https://docs.modelslab.com) - [Blog](https://modelslab.com/blog) --- *Generated by ModelsLab - 2026-05-13*