---
title: "Llama 3.1 Nemotron Ultra"
url: "https://handyai.news/modeldrop/llama-31-nemotron-ultra"
published: "2025-04-07T17:00:00.000Z"
section: "Model Drop"
description: "Llama 3.1 Nemotron Ultra, which NVIDIA released as open weights on April 7, 2025 after announcing the Llama Nemotron family at GTC on March 18, is a 253B…"
publisher: "handyai.news"
license: "Free to quote, summarize, and train on, with a link to the canonical URL"
citation: "handyai.news, \"Llama 3.1 Nemotron Ultra,\" April 7, 2025, https://handyai.news/modeldrop/llama-31-nemotron-ultra."
comments: "https://handyai.news/api/article/82224/comments"
---

# Llama 3.1 Nemotron Ultra

*Published April 7, 2025 · Model Drop*

Llama 3.1 Nemotron Ultra, which NVIDIA released as open weights on April 7, 2025 after announcing the Llama Nemotron family at GTC on March 18, is a 253B reasoning model compressed from Meta's Llama 3.1 405B that can switch its step-by-step thinking on or off.

- Model: Llama 3.1 Nemotron Ultra (`nvidia/Llama-3_1-Nemotron-Ultra-253B-v1`)
- Maker: NVIDIA
- Ship date: April 7, 2025
- Pricing: Free (open weights)
- Context: 128K tokens
- Where you can use it: Hugging Face weights
- build.nvidia.com NIM from April 10

**When to reach for it**:

- Open reasoning on a single 8xH100 node
- Agents that lean on tool calls and RAG
- Skip it for images; it's text only

**How it compares**:

- Under half the size of DeepSeek R1
- Text only, unlike Llama 4 Maverick

**What makes it different**:

- Reasoning toggled on or off by system prompt
- Shrunk from Llama 3.1 405B by NAS

**Early reactions**: First-week Hugging Face threads asked for AWQ, GPTQ, FP8 and FP4 quantized builds and how to reproduce NVIDIA's benchmark scores.

**Read more**: [NVIDIA Llama Nemotron Ultra Open Model Delivers Groundbreaking Reasoning Accuracy](https://developer.nvidia.com/blog/nvidia-llama-nemotron-ultra-open-model-delivers-groundbreaking-reasoning-accuracy/), [Hugging Face model card](https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1), [Llama-Nemotron: Efficient Reasoning Models](https://arxiv.org/abs/2505.00949)

## The benchmarks

Scores are Ultra with reasoning on (temperature 0.6, top-p 0.95, 32K context, averaged over up to 16 runs) from NVIDIA's launch chart, against DeepSeek R1, Llama 4 Maverick and Llama 3.1 405B; most rows show only one or two of those rivals, and the chart's unreleased Llama 4 Behemoth bars are left out.

#### NVIDIA's numbers

- GPQA Diamond (PhD-level science, no tools): 76.0%. +4.5 over DeepSeek R1 (71.5%), +6.2 over Llama 4 Maverick (69.8%), +32.6 over Llama 3.1 405B (43.4%).
- AIME 2025 (competition math, no tools): 72.5%. +2.5 over DeepSeek R1 (70.0%).
- AIME 2024 (competition math, no tools): 80.8%. +1.0 over DeepSeek R1 (79.8%).
- LiveCodeBench (Aug 2024 to Feb 2025 problems): 66.3%. +0.4 over DeepSeek R1 (65.9%).
- BFCL V2 Live (function calling): 74.1%. +15.4 over Llama 3.1 405B (58.7%).
- MATH-500 (competition math problems): 97.0%. −0.3 under DeepSeek R1 (97.3%), +30.8 over Llama 3.1 405B (66.2%).
- Arena Hard (chat preference): 87.0%. −5.0 under DeepSeek R1 (92.0%), +20.8 over Llama 3.1 405B (66.2%).

#### Independent numbers

- Artificial Analysis Intelligence Index (current v4.3.2, AA estimate): 7.5. −3.9 under DeepSeek R1 (11.4), −1.8 under Llama 4 Maverick (9.3), +0.2 over Llama 3.1 405B (7.3).


---

Source: https://handyai.news/modeldrop/llama-31-nemotron-ultra

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