[whatmodel.sh](../../README.md) / Nemotron 3.5 Lightning 30B-A3B
# Nemotron 3.5 Lightning 30B-A3B
NVIDIA · released 2026-08 · 31.6B params (3.6B active) · openmdw-1.1
**score 62.1** ±2.0 · #179 of 732 · from 3 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 3.5 Lightning 30B-A3B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇█▇●▄▃▁ #134 of 469* 37% 1
chat & writing ▁▁▁▃▆▄▆█●▇▇▃ #162 of 402* 41% 1
coding · #134 of 469 · 37% of its score
Beats [Qwen3.5 Plus](qwen3-5-plus.md), [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Seed-OSS 36B Instruct](seed-oss-36b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Luau Leetcode fully solved (Torpe… 52.9% ▁▁▁▁·▁▂●▅▃█▇ beats 11/49 hard · avg 17% 37% sharp · at its level
chat & writing · #162 of 402 · 41% of its score
Beats [MiniMax-M2](minimax-m2.md), [MiniCPM5 2B](minicpm5-2b.md), [Granite 4.2 30B](granite-4-2-30b.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [LFM2.5 2.6B](lfm2-5-2-6b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1353.3 ▁▁▁▂▃▃▃▆●▇█▃ beats 197/385 avg 1260 41% blunt
other tests (1)
test result vs the others in its run difficulty counts why
RobloxQA v2.0 multiple choice (To… 87.7% ▁▁▃●█▄▃▃▆▂▂▂ beats 7/49 easy · avg 77% 20% typical
source results share of the score
Roblox LLM Leaderboard 2 57% ▇▇▇▇▇▇▇
LMArena leaderboard dataset 1 41% ▇▇▇▇▇
prior: pull toward the middle 2% ▇
weighted average = 62.1
Licences: Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### Through an API
api (Fireworks AI) · $0.088/M blended · $0.05 in / $0.2 out per 1M tokens · [fireworks.ai](https://fireworks.ai/docs/)
[price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/fireworks-ai/models/accounts/fireworks/models/nemotron-lightning-3p5-30b-a3b.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/nvidia/nemotron-3.5-lightning.toml)
### 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 3.5 Lightning 30B-A3B (Q4_K_M, needs ~25.5 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF/tree/f0eec2267ae843d9eb21ea3926ab0046da0a8628))
```sh
llama cli -hf bartowski/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-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-3.5-Lightning-30B-A3B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~35 GB) `llama cli -hf bartowski/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-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 3.5 Lightning 30B-A3B (Q4_K_M, needs ~25.5 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF/tree/f0eec2267ae843d9eb21ea3926ab0046da0a8628))
```sh
lms get https://huggingface.co/bartowski/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-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 (~35 GB) `lms get https://huggingface.co/bartowski/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF@Q8_0 -y` · 4bit (~17.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-4bit -y` · 8bit (~33.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-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 Nemotron 3.5 Lightning 30B-A3B (Q4_K_M, needs ~19.46 GB, from [ollama.com](https://ollama.com/library/nemotron-3.5-lightning/tags))
```sh
ollama run nemotron-3.5-lightning:30b
```
Other versions: Q8_0 (~38.42 GB) `ollama run nemotron-3.5-lightning:30b-a3b-q8_0` · nvfp4 (~19.46 GB, Mac only) `ollama run nemotron-3.5-lightning:30b-a3b-mlx`
### 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 3.5 Lightning 30B-A3B (4bit, needs ~17.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-4bit/tree/55ac8c89261109b36c04371cd3f479a4594208c8))
```sh
mlx_lm.chat --model mlx-community/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-4bit
```
Other versions: 8bit (~33.6 GB, Mac only) `mlx_lm.chat --model mlx-community/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-8bit`
Model repository and documentation: [huggingface.co/nvidia](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16)
## Published speed measurements
Reference results for the hardware, engine and settings below. These are not verified speeds of the downloads above or predictions for your machine.
hardware precision speed engine and settings
Apple M2 Pro, 32 GB q4_k_m 40.53 tok/s ollama (version not recorded) · l…
DGX Spark (GB10), 122 GB q4_k_m 87.08 tok/s ollama (version not recorded) · l…
Each speed links to its source, which has the full settings.
[interactive version](https://whatmodel.sh/model/nemotron-3-5-lightning) · [JSON](https://whatmodel.sh/api?format=json&all=1)