[whatmodel.sh](../../README.md) / Llama 3.3 Nemotron Super 49B v1.5
# Llama 3.3 Nemotron Super 49B v1.5
NVIDIA · released 2025-07 · 49B params · nvidia-open-model
**score 57.0** ±1.4 · #244 of 732 · from 4 benchmark results across 2 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Llama 3.3 Nemotron Super 49B v1.5 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #211 of 469 79% 3
chat & writing ▁▁▁▃▆▄▆█●▇▇▃ #174 of 402* 20% 1
coding · #211 of 469 · 79% of its score
Beats [Qwen3.5 4B](qwen3-5-4b.md), [Falcon-H1R 7B](falcon-h1r-7b.md), [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md), [DeepSeek-Coder-V2 Instruct (0724)](deepseek-coder-v2-0724.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench Pro 2025Q2 (Medium)… 6.1% ▃▃····▃·●·▃▃ beats 4/7 very hard · avg 2.6% 44% sharp · too hard for it
LiveCodeBench Pro 2025Q2 (Easy) (… 61.9% ▄·▄····▄●▄█▄ beats 3/7 medium · avg 46% 24% at its level
LiveCodeBench v6 (Aug 2024–May 20… 68.1% ▄▄···▄·●▄█·▄ beats 3/7 medium · avg 58% 10% at its level
chat & writing · #174 of 402 · 20% of its score
Beats [MiniCPM5 2B](minicpm5-2b.md), [Granite 4.2 30B](granite-4-2-30b.md), [Seed-OSS 36B Instruct](seed-oss-36b.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1343.1 ▁▁▁▂▃▃▃▆●▇█▃ beats 182/385 avg 1260 20% blunt
source results share of the score
NVIDIA (submitted / maker-reported) 3 79% ▇▇▇▇▇▇▇▇▇
LMArena leaderboard dataset 1 20% ▇▇
prior: pull toward the middle 1% ▇
weighted average = 57.0
Licences: NVIDIA ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Llama 3.3 Nemotron Super 49B v1.5 (ud-q4_k_xl, needs ~29.9 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF/tree/main))
```sh
llama cli -hf unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF:UD-Q4_K_XL
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF:UD-Q4_K_XL
```
Other versions: ud-q2_k_xl (~15.2 GB) `llama cli -hf unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF:UD-Q2_K_XL`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Llama 3.3 Nemotron Super 49B v1.5 (ud-q4_k_xl, needs ~29.9 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF/tree/main))
```sh
lms get https://huggingface.co/unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF@UD-Q4_K_XL -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: ud-q2_k_xl (~15.2 GB) `lms get https://huggingface.co/unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF@UD-Q2_K_XL -y` · 4bit (~28.1 GB, Mac only) `lms get https://huggingface.co/jesusoctavioas/Llama-3_3-Nemotron-Super-49B-v1_5-mlx-4Bit -y` · 8bit (~53 GB, Mac only) `lms get https://huggingface.co/LogicBombaklot/Llama-3_3-Nemotron-Super-49B-v1_5-mlx-8Bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Llama 3.3 Nemotron Super 49B v1.5 (ud-q4_k_xl, needs ~29.9 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF/tree/main))
```sh
ollama run hf.co/unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF:UD-Q4_K_XL
```
Other versions: ud-q2_k_xl (~15.2 GB) `ollama run hf.co/unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF:UD-Q2_K_XL`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Llama 3.3 Nemotron Super 49B v1.5 (4bit, needs ~28.1 GB, Mac only, from [huggingface.co/jesusoctavioas](https://huggingface.co/jesusoctavioas/Llama-3_3-Nemotron-Super-49B-v1_5-mlx-4Bit/tree/1d51cdfe2a6c188d4d565754effb6ae8ec43bdad))
```sh
mlx_lm.chat --model jesusoctavioas/Llama-3_3-Nemotron-Super-49B-v1_5-mlx-4Bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model jesusoctavioas/Llama-3_3-Nemotron-Super-49B-v1_5-mlx-4Bit
```
Other versions: 8bit (~53 GB, Mac only) `mlx_lm.chat --model LogicBombaklot/Llama-3_3-Nemotron-Super-49B-v1_5-mlx-8Bit`
Model repository and documentation: [huggingface.co/nvidia](https://huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1_5)
[interactive version](https://whatmodel.sh/model/llama-3-3-nemotron-super-49b-v1-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)