whatmodel.sh / Llama 3.1 Nemotron 51B Instruct

Llama 3.1 Nemotron 51B Instruct

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Llama 3.1 Nemotron 51B Instruct is an open-weights language model from NVIDIA, released September 2024. Its published size is 51.5B parameters. Licence: nvidia-open-model-license.

Catalogue identity notes

Comparison-only. Arena entry 'llama-3.1-nemotron-51b-instruct' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'llama-3.1-nemotron-51b-instruct' as 'Llama-3.1-Nemotron-51B-Instruct' (Nvidia, Llama 3.1), linking https://huggingface.co/nvidia/Llama-3_1-Nemotron-51B-Instruct. Maker repo nvidia/Llama-3_1-Nemotron-51B-Instruct @f4d9431: licence tag other (nvidia-open-model-license). 51,501,015,040 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-09: month the maker's Hugging Face repo was created (createdAt 2024-09-22).

51.0±1.8#325 of 719 LLMsfrom 6 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Llama 3.1 Nemotron 51B Instruct, out of 828 with a score, then more from NVIDIA.

#modelscorewhereRAMreleased
391OpenCoder-8B-Instruct INF (infly)51±1local~6.4GB2024-11
392Command A Translate (08-2025) Cohere51±5api price unknown2025-08
393Llama 3.1 Nemotron 51B Instruct NVIDIA51±2local~34GB2024-09
394Ministral 3 3B Reasoning 2512 Mistral51±1local~3.5GB2025-12
395Qwen3 4B Alibaba51±1local~3.7GB2025-04
more from NVIDIA
98Nemotron 3 Ultra 550B-A55B NVIDIA69±1api $1.35/M +local~89GB2026-06
155Nemotron Cascade 2 30B-A3B NVIDIA66±1local~20GB2026-03
168Nemotron 3 Super 120B-A12B NVIDIA65±1api $0.28/M +local~73GB2026-03

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #240 of 393 * · 28% of its score · 1 test

Beats Mistral Small 4 (119B-A6.5B), Ministral 3 14B Reasoning 2512, Qwen2.5 Coder 32B Instruct here, though they rank higher overall. Trails Flan-T5 XXL, Granite 4.0 H 1B, LFM2.5 1.2B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1286.6 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 116/379avg 1268▇▇▇▇▇▇▇▇▇▇▇ 28%blunt
math▁▂▃▅▆▅▄█▄▁▁▁ #232 of 398 * · 18% of its score · 1 test

Beats GPT-4o (2024-08-06), Command A (03-2025), GPT-4o (2024-11-20) here, though they rank higher overall. Trails OpenThinker 7B, Olmo 3 7B Instruct, Tulu 2 70B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
GSM8K lab91.4% ✎▂▂▂▅▅▆▅▇█▇▄▃ beats 129/146very easy · avg 89%▇▇▇▇▇▇▇ 18%sharp · small run
other tests4 not in a task · 53% of its score
testresultvs the others in its rundifficultycountswhy
ARC Challenge lab69.2% ✎▆▁▁▂▂▅▇█▄▃▃▂ beats 79/97medium · avg 70%▇▇▇▇▇▇▇▇ 19%sharp · at its level · small run
MMLU lab80.2% ✎▁▁▁▁▁▂▃▅█▃▆▃ beats 137/183easy · avg 78%▇▇▇▇▇ 14%at its level · small run
WinoGrande lab84.5% ✎▁···▂▁▁▅█▇▃▃ beats 85/87easy · avg 85%▇▇▇▇▇ 12%at its level · small run
HellaSwag lab85.6% ✎▁▁▁▁▂▂▃▇█▆▅▃ beats 79/85easy · avg 86%▇▇▇▇ 8.9%small run

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Llama 3.1 Nemotron 51B Instruct · needs ~31.04 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Llama 3.1 Nemotron 51B Instruct · needs ~31.04 GB · from huggingface.co/bartowski

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Llama 3.1 Nemotron 51B Instruct · needs ~31.04 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF:Q4_K_M

Official weights and documentation: huggingface.co/nvidia ↗

$ how the score was worked out · Puzzle: Distillation-Based NA… 71%, LMArena leaderboard dataset 28%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs (arXiv:2411.19146v5) · lab-reported5▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 71%CC-BY-4.0
LMArena leaderboard dataset1▇▇▇▇▇▇▇▇ 28%CC BY 4.0
prior · pull toward the middle▇ 1%
weighted average= 51.0

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json