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Available now on ModelsLab · Language Model

NVIDIA: Llama 3.3 Nemotron Super 49B V1.5Reason Fast. Fit Single GPU

Try NVIDIA: Llama 3.3 Nemotron Super 49B V1.5API Documentation

Optimize Accuracy and Speed

128K Context

Handle Long Workloads

Process 128K tokens for RAG, multi-step planning, and agent coherence in NVIDIA: Llama 3.3 Nemotron Super 49B V1.5.

NAS Architecture

Slash Memory Footprint

Neural Architecture Search reduces VRAM, runs on H200 GPU for NVIDIA: Llama 3.3 Nemotron Super 49B V1.5 API efficiency.

RL Fine-Tuning

Master Tool Calling

RLVR and DPO enhance reasoning, chat, and tools in NVIDIA: Llama 3.3 Nemotron Super 49B V1.5 model.

Examples

See what NVIDIA: Llama 3.3 Nemotron Super 49B V1.5 can create

Copy any prompt below and try it yourself in the playground.

Math Proof

“Prove the Pythagorean theorem step-by-step, using chain-of-thought reasoning and verifiable intermediate steps.”

Code Debugger

“Debug this Python function for sorting linked lists: [insert buggy code]. Explain fixes with tool calls if needed.”

Science Summary

“Summarize quantum entanglement from 10K-token input documents, citing key equations and experiments.”

Agent Plan

“Plan a multi-step workflow: research market trends, call analysis tool, generate report with 128K context.”

For Developers

A few lines of code.
Reasoning agents. One 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 
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 NVIDIA: Llama 3.3 Nemotron Super 49B V1.5

Read the docs

49B-parameter LLM derived from Llama-3.3-70B-Instruct, post-trained for reasoning, chat, RAG, and tool calling. Uses NAS for efficiency on single H200 GPU. Supports 128K context.

Neural Architecture Search skips attention blocks and optimizes FFNs to cut memory and boost throughput. Fits high workloads on one GPU. Balances accuracy with tokens-per-second.

SFT on math, code, science, tools; RL stages include RPO for chat, RLVR for reasoning, DPO for tool use. Derived from Meta Llama-3.3-70B-Instruct.

Vision supported per some providers. Core is text-based LLM with function calling. Context up to 131K tokens, max output 4K.

128K-131K tokens standard. Enables long-term coherence for agents and retrieval. Output up to 131K in some setups.

NVIDIA NIM, AWS Marketplace, OpenRouter, DeepInfra. Runs on Transformers/vLLM with reasoning modes. Single-GPU friendly.

Ready to create?

Start generating with NVIDIA: Llama 3.3 Nemotron Super 49B V1.5 on ModelsLab.

Try NVIDIA: Llama 3.3 Nemotron Super 49B V1.5API Documentation