---
title: Nemotron Super 49B — Reasoning LLM | ModelsLab
description: Access NVIDIA Llama 3.3 Nemotron Super 49B V1.5 for efficient reasoning, tool calling, and 128K context. Try agentic workflows via API now.
url: https://modelslab.com/nvidia-llama-33-nemotron-super-49b-v15
canonical: https://modelslab.com/nvidia-llama-33-nemotron-super-49b-v15
type: website
component: Seo/ModelPage
generated_at: 2026-04-30T08:07:13.453078Z
---

Available now on ModelsLab · Language Model

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

[Try NVIDIA: Llama 3.3 Nemotron Super 49B V1.5](/models/open_router/nvidia-llama-3.3-nemotron-super-49b-v1.5) [API Documentation](https://docs.modelslab.com)

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](/models/open_router/nvidia-llama-3.3-nemotron-super-49b-v1.5).

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 ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

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

FAQ

Common questions about NVIDIA: Llama 3.3 Nemotron Super 49B V1.5
---

[Read the docs ](https://docs.modelslab.com)

### What is NVIDIA: Llama 3.3 Nemotron Super 49B V1.5?

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.

### How does nvidia llama 3.3 nemotron super 49b v1 5 API improve efficiency?

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.

### What training enhances NVIDIA: Llama 3.3 Nemotron Super 49B V1.5 model?

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.

### Does NVIDIA: Llama 3.3 Nemotron Super 49B V1.5 alternative support vision?

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

### What is the context length for nvidia llama 3.3 nemotron super 49b v1 5?

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

### Where to deploy NVIDIA: Llama 3.3 Nemotron Super 49B V1.5 LLM?

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 V1.5](/models/open_router/nvidia-llama-3.3-nemotron-super-49b-v1.5) [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 - 2026-04-30*