Instructions to use engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M

Ollama
How to use engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF with Ollama:
```
ollama run hf.co/engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF

NVIDIA released Nemotron 3.5 Lightning as a text-only agent model. This repository is Lightning with eyes and ears: the C-RADIO vision tower and the Parakeet audio tower from NVIDIA's own Nemotron-3-Nano-Omni, attached through their unified projector, quantized for llama.cpp, and tested modality by modality before upload. Image, audio, and video understanding are the released, gated capabilities. One-pass video WITH its soundtrack also works zero-shot; there is one prompting note (below) for getting both channels reliably in a single answer.

No training was involved, and that is the interesting part: Lightning shares the exact backbone geometry of the model those projectors were trained for, so perception transfers zero-shot. Details in How This Was Made below.

Model Summary


Base LLM	NVIDIA Nemotron 3.5 Lightning 30B-A3B (hybrid Mamba-2 + MoE + attention, 3B active, 1M context)
Vision tower	C-RADIO ViT-H, lifted from Nemotron-3-Nano-Omni
Audio tower	Parakeet FastConformer-XL encoder, lifted from Nemotron-3-Nano-Omni
Video	C-RADIO per frame + temporal 2-frame patches + EVS token pruning
Projector	one unified mmproj file, both towers, upstream Parakeet tensor layout
Modalities in	text, image, audio, video, and one-pass video+audio (all released; one-pass has a prompting note)
Runs on	llama.cpp (see compatibility matrix below), from Jetson to DGX Spark to datacenter

Quick Start

Download a quant plus the projector, then:

./llama-server -m Nemotron-3.5-Lightning-Omni-30B-A3B-Q4_K_M.gguf --mmproj mmproj-omni-vision-audio-v2-F16.gguf

Open http://localhost:8080 and drag in a photo, a wav, or an mp4. The same endpoint serves /v1/chat/completions with image_url, input_audio, and video content parts.

llama.cpp compatibility

Capability	Stock llama.cpp (current master)	llama.cpp-omni fork b10598
Text	yes	yes
Image	yes	yes
Audio	yes	yes
Video	no for Nemotron models (upstream's video path covers Qwen-VL style; the Nemotron temporal+EVS graph is fork-only)	yes, gated per quant
Video with soundtrack, one pass	no for Nemotron models	yes; ask for both channels explicitly, see Verification

Prebuilt fork binaries for Linux arm64, Linux x64, Win x64:

https://github.com/engram-ae/llama.cpp-omni/releases/tag/b10598

Files

Quantizations land one by one as each passes its gates; this table and TEST-MATRIX.md grow together until all nine tiers are present.

File	Size	Verified	Video
Q3_K_M	20.7 GB	gated: text, image, audio	see matrix
IQ3_M	18.9 GB	gated: text, image, audio	see matrix
IQ4_XS	19.0 GB	gated: text, image, audio	see matrix
MXFP4_MOE	18.7 GB	gated: text, image, audio	see matrix
Q4_K_M	25.4 GB	gated: text, image, audio	see matrix
Q5_K_M	27.0 GB	gated: text, image, audio	see matrix
Q6_K	34.9 GB	gated: text, image, audio	see matrix
Q8_0	35.0 GB	gated: text, image, audio	see matrix
mmproj (unified, all tiers)	3.0 GB	both towers	required

The unified projector mmproj-omni-vision-audio-v2-F16.gguf serves every tier; single-tower use is possible but the unified file is the intended path.

Every quantization in this table passed the text, image, audio, and silent-video gates before upload. The one-pass audio+video column is recorded per quant in TEST-MATRIX.md and is experimental in this revision.

Verification

Claims are cheap; here is what was measured, on a DGX Spark (GB10):

Five gates per quantization at temperature 0, graded against known ground truths, published in TEST-MATRIX.md. Text, image, and audio must pass for a file to ship; the video columns are recorded honestly and do not block, because video is experimental here.
One-pass A/V, the measured truth (2026-08-25, corrected twice as evidence came in): first hypothesis was temporal-pathway drift, killed by measurement. Second hypothesis was that audio irreversibly dominates, also killed by measurement. What actually happens: video-only works at every length (2 to 64 frames, matches the donor), and in one pass WITH a soundtrack both channels are fully present and attendable. With a NEUTRAL single-subject prompt ("what is in the video?") zero-shot Lightning tends to answer from the audio and under-report the picture. With a prompt that asks for BOTH, it returns both correctly, every time tested. Two verified examples, temp 0: "Describe what is shown on screen AND transcribe what the narrator says" and "Give me two things: (1) the on-screen visuals, (2) the spoken words" each produced the full visual description AND the full transcription in a single answer. So one-pass A/V is a real, released capability with a prompting note, not a broken feature. The donor Omni, co-trained on interleaved A/V, balances both even at a neutral prompt; a future Lightning projector tune could shift the neutral-prompt default the same way, but it is polish, not a fix for something broken.
ASR transfer cost, measured: on the 38-word gate narration, the donor Omni model scores 2.6 percent word error rate (one proper noun); this zero-shot assembly scores 7.9 percent (three proper nouns, including the same one the donor misses). Single-clip gate, stated at exactly that scope, not a benchmark.
Decode speed, measured on the same machine and binary:

Model (same binary, DGX Spark GB10)	Decode
Lightning-Omni, community UD-Q4_K_M flavor	61.1 tok/s
Donor Nemotron-3-Omni, Q4_K_M	68.7 tok/s

The projector adds no decode cost (it runs once at encode). Numbers for this repository's own quantizations land here as they are measured.

The underlying video and audio inference graphs were validated against NVIDIA's PyTorch reference during the fork's development (video 0.0019 percent relative L2 with exact EVS parity; audio transcribing the reference narration word for word). See the fork repository for that work: https://github.com/engram-ae/llama.cpp-omni

Benchmarks

The language model weights are byte-identical to NVIDIA's release, so NVIDIA's published text benchmarks describe this model's brain at BF16: SWE-bench Verified 51.56, GPQA Diamond 75.44, MMLU Pro 81.94 (their numbers, their card: https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16). Quantization effects on those scores are not measured here; the BF16 file in this repository is the reference conversion.

Multimodal quality is what this repository adds, and it is gated rather than benchmarked: see TEST-MATRIX.md and the measured ASR note above.

Model Architecture

The assembly, exactly:

Part	Geometry	Origin
Language backbone	nemotron_h hybrid Mamba-2/MoE/attention, hidden 2688, 52 layers, vocab 131072, 30B total / 3B active	NVIDIA Nemotron 3.5 Lightning, unmodified
Vision tower	C-RADIO ViT-H, patch 16, 512px, hidden 1280	Nemotron-3-Nano-Omni
Audio tower	Parakeet FastConformer-XL, 24 layers, hidden 1024, 128 mel	Nemotron-3-Nano-Omni
Video temporal	2-frame channel-concat patch embedding, stored as v.patch_embd.weight.1 (16x16x6x1280), plus EVS token pruning	Nemotron-3-Nano-Omni
Projectors	vision: pixel-shuffle then MLP to 2688; audio: MLP to 2688; both in one mmproj	Nemotron-3-Nano-Omni

How This Was Made (and why no training was needed)

NVIDIA trained the Nano-Omni projectors to translate C-RADIO and Parakeet features into the embedding space of a nemotron_h backbone with hidden size 2688, 52 layers, and a 131072 vocabulary. Nemotron 3.5 Lightning has exactly that geometry: it is a sibling checkpoint of the same architecture family. Attaching the projector to Lightning is therefore a compatible operation, and it works zero-shot: the perception pathway transfers, and the measured cost is two additional word errors on the 38-word ASR gate (see Verification), concentrated in rare proper nouns. Image understanding matches the donor exactly; video is where the drift concentrates, which is why it ships as experimental here.

A projector fine-tune against Lightning specifically is the known path to closing that gap and is planned as a follow-up revision.

Input

Text prompts; images (png/jpg/webp); audio (wav/mp3); video files (mp4 and other ffmpeg-readable containers), with or without a soundtrack. One media file per turn.

Output

Text.

Software Integration

Runtime: llama.cpp. Full modality support in the llama.cpp-omni fork, release b10598 (prebuilt Linux arm64 CUDA 13 binaries included). Text, image, and audio also run on stock llama.cpp current master with these same files.
Tested on: NVIDIA DGX Spark (GB10, CUDA 13, sm_121).

License / Terms of Use

Language model weights: NVIDIA Nemotron 3.5 Lightning, released by NVIDIA under the Open Model, Data and Weights License 1.1 (OpenMDW-1.1): https://openmdw.ai/license/1-1/
Encoder towers and projector weights: lifted from NVIDIA Nemotron-3-Nano-Omni, governed by the NVIDIA Open Model License terms of that release.
The GGUF conversion, the quantizations, the unified projector packaging, and the llama.cpp video/one-pass graphs are my work; the underlying weights remain NVIDIA's under their respective licenses.

Model Version(s)

v1 (2026-08): zero-shot assembly, nine quantizations, five-gate matrix.
Planned v1.1: Lightning-specific projector fine-tune (closes the measured ASR proper-noun gap and the experimental-video seam).

Known Limitations

One-pass audio+video works, with a prompting note: a neutral single-subject prompt may answer from the audio and under-report the picture. Ask for both channels explicitly (see Verification) and it returns both correctly. Video-only and audio-only are unconditionally reliable.
ASR smudges rare proper nouns more than the donor Omni does (measured above). General transcription is faithful.
Multimodal gates are functional checks, not benchmark suites. Treat TEST-MATRIX.md as exactly what it is: proof each file works, per modality, on real inputs.
Video and one-pass A/V require the fork binaries until the planned upstream PR lands.

Release Date

2026-08 (Hugging Face).

Downloads last month: 918

GGUF

Model size

33B params

Architecture

nemotron_h_moe

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for engram-ae/Nemotron-3.5-Lightning-Omni-30B-A3B-GGUF

Base model

nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16

Quantized

(113)

this model