[whatmodel.sh](../../README.md) / Nemotron-Cascade-8B-Thinking
# Nemotron-Cascade-8B-Thinking
NVIDIA · released 2025-12 · 8.19B params · nvidia-open-model-license
**score 56.8** ±1.5 · #248 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 Nemotron-Cascade-8B-Thinking among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇█●▅▄▃▁ #204 of 469 94% 3
math ▁▁▃▅█●▄█▄▂▁▁ #202 of 431* 4.8% 1
coding · #204 of 469 · 94% of its score
Beats [Gemini 2.5 Flash](gemini-2-5-flash.md), [Qwen3 30B-A3B Instruct 2507](qwen3-30b-a3b-instruct-2507.md), [Qwen3.5 4B](qwen3-5-4b.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 3/7 very hard · avg 2.6% 52% sharp · too hard for it
LiveCodeBench Pro 2025Q2 (Easy) (… 64.8% ▄·▄····▄▄●█▄ beats 4/7 medium · avg 46% 29% at its level
LiveCodeBench v6 (Aug 2024–May 20… 71.4% ▄▄···▄·▄▄●·▄ beats 6/7 medium · avg 58% 13% at its level
math · #202 of 431 · 4.8% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [Qwen3 235B-A22B (Apr 2025, hybrid)](qwen3-235b-a22b.md) here, though they rank higher overall. Trails [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md), [Olmo 3 7B Instruct](olmo-3-7b-instruct.md), [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K 93.0% ▃··▃··▃▃·●·█ beats 4/7 very easy · avg 93% 4.8% sharp · maxed out · noisy
source results share of the score
NVIDIA (submitted / maker-reported) 3 94% ▇▇▇▇▇▇▇▇▇▇▇
Unsupervised Confidence Calibration for… 1 5% ▇
prior: pull toward the middle 1% ▇
weighted average = 56.8
Licences: NVIDIA ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generation ([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 Nemotron-Cascade-8B-Thinking (Q4_K_M, needs ~5.03 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/nvidia_Nemotron-Cascade-8B-Thinking-GGUF/tree/92a27d2d91739082140235872b16745e47517adf))
```sh
llama cli -hf bartowski/nvidia_Nemotron-Cascade-8B-Thinking-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/nvidia_Nemotron-Cascade-8B-Thinking-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.71 GB) `llama cli -hf bartowski/nvidia_Nemotron-Cascade-8B-Thinking-GGUF:Q8_0`
### 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 Nemotron-Cascade-8B-Thinking (Q4_K_M, needs ~5.03 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/nvidia_Nemotron-Cascade-8B-Thinking-GGUF/tree/92a27d2d91739082140235872b16745e47517adf))
```sh
lms get https://huggingface.co/bartowski/nvidia_Nemotron-Cascade-8B-Thinking-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~8.71 GB) `lms get https://huggingface.co/bartowski/nvidia_Nemotron-Cascade-8B-Thinking-GGUF@Q8_0 -y` · 4bit (~4.6 GB, Mac only) `lms get https://huggingface.co/introvoyz041/Nemotron-Cascade-8B-Thinking-mlx-4Bit -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 Nemotron-Cascade-8B-Thinking (Q4_K_M, needs ~5.03 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/nvidia_Nemotron-Cascade-8B-Thinking-GGUF/tree/92a27d2d91739082140235872b16745e47517adf))
```sh
ollama run hf.co/bartowski/nvidia_Nemotron-Cascade-8B-Thinking-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.71 GB) `ollama run hf.co/bartowski/nvidia_Nemotron-Cascade-8B-Thinking-GGUF:Q8_0`
### 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 Nemotron-Cascade-8B-Thinking (4bit, needs ~4.6 GB, Mac only, from [huggingface.co/introvoyz041](https://huggingface.co/introvoyz041/Nemotron-Cascade-8B-Thinking-mlx-4Bit/tree/0fa2c6803290ec6578403826210c258df9c7d1a7))
```sh
mlx_lm.chat --model introvoyz041/Nemotron-Cascade-8B-Thinking-mlx-4Bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model introvoyz041/Nemotron-Cascade-8B-Thinking-mlx-4Bit
```
Model repository and documentation: [huggingface.co/nvidia](https://huggingface.co/nvidia/Nemotron-Cascade-8B-Thinking)
[interactive version](https://whatmodel.sh/model/nemotron-cascade-8b-thinking) · [JSON](https://whatmodel.sh/api?format=json&all=1)