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

Llamacpp imatrix Quantizations of NVIDIA-Nemotron-3.5-Lightning-30B-A3B by nvidia#

Using llama.cpp release b10362 for quantization.

Original model: https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16

Model details:

• Parameter count: 33B
• Input support: text
• MTP: yes - details
• imatrix: yes - details

How to run

What's new:#

Now actually with MTP!

The earlier upload incorrectly claimed to have MTP added, NOW it does.. For those who downloaded earlier versions and don't want to fully redownload, I've included the separated files for Q8_0 and Q4_0, sorry about that!

Prompt format#

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>

Don't know which to choose? Grab Q4_K_M (25.48GB) - usually a good mix of size and performance. Download instructions available here

Available files:#

Download a specific file:

hf download bartowski/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF --include "NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Q4_K_M.gguf" --local-dir ./

Downloading using the Hugging Face CLI#

Click to view download instructions

First, make sure you have the Hugging Face CLI installed:

pip install -U "huggingface_hub[cli]"

Download a specific file:

hf download bartowski/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF --include "NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Q4_K_M.gguf" --local-dir ./

The files marked true in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:

hf download bartowski/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF --include "NVIDIA-Nemotron-3.5-Lightning-30B-A3B-bf16/*" --local-dir ./

You can either specify a new local-dir (NVIDIA-Nemotron-3.5-Lightning-30B-A3B-bf16) or download them all in place (./)

How to run#

These quants run with llama.cpp - installable in one line via llama.app:

curl -LsSf https://llama.app/install.sh | sh
llama-server -hf bartowski/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M

llama-server includes a built-in chat web UI, served at http://localhost:8080 by default.

These quants were made with llama.cpp release b10362 - if this model's architecture is newly supported, you'll need that release or newer to run them.

They also work in: LM Studio · koboldcpp · ramalama · Jan AI · Text Generation Web UI · LoLLMs · Atomic Chat

MTP#

This model has MTP (Multi-Token Prediction) layers, and they are included in these quants

MTP layers act as a built-in draft model, letting llama.cpp run speculative decoding for faster generation. To use them, add the following flag to your llama.cpp command:

--spec-type draft-mtp

Note: the MTP layers are stored at Q4_0 in the imatrix quants (except for the Q8_0 quant), since imatrix calibration does not exercise them. Q4_0 is chosen for its speed which massively benefits MTP performance.

imatrix#

All quants made using imatrix option with dataset from here. The imatrix is available here: NVIDIA-Nemotron-3.5-Lightning-30B-A3B-imatrix.gguf.

Embed/output weights#

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

ARM/AVX information#

llama.cpp automatically "repacks" weights into an interleaved layout at load time for faster inference on ARM and AVX machines - details in this PR. This once required downloading special Q4_0_4_4/4_8/8_8 files; those are long gone. Online repacking now covers Q4_0, IQ4_NL, and most K-quants, so no special quant choice is needed for CPU inference.

Which file should I choose?#

Click here for details

An older (early 2024) but still useful write-up with charts comparing quant performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Hugging Face can also do this math for you: add your hardware in your Local Apps settings and the model page will show which files fit.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

Credits#

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

Thank you ZeroWw for the inspiration to experiment with embed/output.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski