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Nim/nvidia/llama-3.3-nemotron-super-49b-v1Reason Fast. Fit Single GPU

Try Nim/nvidia/llama-3.3-nemotron-super-49b-v1API Documentation

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.

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 
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

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.

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.

Up to 128K-131K tokens for input and output. Enables long-context reasoning and agent tasks.

Yes, uses CUDA for NVIDIA GPUs. NAS reduces memory for high throughput on H200.

Compare via benchmarks for reasoning/math. This model leads in efficiency-accuracy tradeoff.

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-v1API Documentation