--- title: Nemotron Super 49B — Reasoning LLM | ModelsLab description: Access nim/nvidia/llama-3.3-nemotron-super-49b-v1 API for efficient reasoning, tool calling, and 128K context. Deploy via LLM endpoint now. url: https://modelslab.com/nimnvidiallama-33-nemotron-super-49b-v1 canonical: https://modelslab.com/nimnvidiallama-33-nemotron-super-49b-v1 type: website component: Seo/ModelPage generated_at: 2026-05-05T20:27:43.568448Z --- Available now on ModelsLab · Language Model Nim/nvidia/llama-3.3-nemotron-super-49b-v1 Reason Fast. Fit Single GPU --- [Try Nim/nvidia/llama-3.3-nemotron-super-49b-v1](/models/together_ai/nim-nvidia-llama-3.3-nemotron-super-49b-v1) [API Documentation](https://docs.modelslab.com) Optimize Reasoning Efficiency --- NAS Architecture ### 49B Parameter Efficiency Neural Architecture Search reduces memory footprint for nim/nvidia/llama-3.3-nemotron-super-49b-v1 on single H200 GPU. 128K Context ### Advanced Tool Calling Supports function calling, RAG, and instruction following in nim/nvidia/llama-3.3-nemotron-super-49b-v1 API. High Throughput ### Leading Reasoning Accuracy Balances speed and performance for chat, math, and multi-step tasks via nim nvidia llama 3.3 nemotron super 49b v1. Examples See what Nim/nvidia/llama-3.3-nemotron-super-49b-v1 can create --- Copy any prompt below and try it yourself in the [playground](/models/together_ai/nim-nvidia-llama-3.3-nemotron-super-49b-v1). Code Review “Analyze this Python function for bugs and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2)” Math Proof “Prove that the sum of the first n odd numbers equals n squared. Provide step-by-step reasoning.” JSON Schema “Generate a JSON schema for a user profile with fields: name (string), age (integer 0-120), email (string format), preferences (object with keys color and theme).” RAG Summary “Summarize key insights from these documents on climate change impacts, then answer: What are mitigation strategies?” For Developers A few lines of code. Reasoning LLM. One Endpoint --- 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 Nim/nvidia/llama-3.3-nemotron-super-49b-v1 --- [Read the docs ](https://docs.modelslab.com) ### What is nim/nvidia/llama-3.3-nemotron-super-49b-v1? It is a 49B parameter LLM derived from Llama-3.3-70B-Instruct, optimized via NAS for reasoning and efficiency. Fits on single H200 GPU with 128K context. Supports tool calling and RAG. ### How to use nim/nvidia/llama-3.3-nemotron-super-49b-v1 API? Call the LLM endpoint with model: 'nim/nvidia/llama-3.3-nemotron-super-49b-v1'. Use messages array for system/user roles. Set max_tokens up to 131K. ### What context length supports nim nvidia llama 3.3 nemotron super 49b v1? Up to 128K-131K tokens for input and output. Enables long-context reasoning and agent tasks. ### Is nim/nvidia/llama-3.3-nemotron-super-49b-v1 model GPU optimized? Yes, uses CUDA for NVIDIA GPUs. NAS reduces memory for high throughput on H200. ### Find nim/nvidia/llama-3.3-nemotron-super-49b-v1 alternative? Compare via benchmarks for reasoning/math. This model leads in efficiency-accuracy tradeoff. ### Does nim nvidia llama 3.3 nemotron super 49b v1 api handle function calling? Yes, post-trained for tool calling and instruction following. Integrates with RAG workflows. Ready to create? --- Start generating with Nim/nvidia/llama-3.3-nemotron-super-49b-v1 on ModelsLab. [Try Nim/nvidia/llama-3.3-nemotron-super-49b-v1](/models/together_ai/nim-nvidia-llama-3.3-nemotron-super-49b-v1) [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 - 2026-05-06*