---
library_name: gguf
base_model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
tags:
  - gguf
  - turboquant
  - kv-cache-quantization
  - nemotron
  - nvidia
  - mamba2
  - hybrid
  - moe
  - llama-cpp
  - quantized
license: other
license_name: nvidia-open-model-license
license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
---

# Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M

GGUF Q4_K_M weight-quantized variant of [nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) with **TurboQuant** KV cache compression for efficient inference with llama.cpp, Ollama, and LM Studio. Features a hybrid Mamba-2 + Transformer MoE architecture with 30.7B total parameters (3.2B active per token) and up to 1M context length.

## Overview

This model combines two compression techniques:
- **GGUF Q4_K_M weight quantization** -- reduces model size from ~60 GB to ~14 GB
- **TurboQuant KV cache compression** -- block-diagonal rotations (Clifford algebra) for 3-bit KV cache, 5.3x faster prefill

## Quickstart

### llama.cpp
```bash
llama-cli -m Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M.gguf \
  --cache-type-k q4_0 --cache-type-v q4_0 \
  -p "Explain quantum computing"
```

### Ollama
```bash
ollama run majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q4_K_M
```

### LM Studio
Download the GGUF file and load in LM Studio. Enable TurboQuant KV cache in advanced settings.

## Specifications

| Property | Value |
|----------|-------|
| Base Model | nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 |
| Parameters | 30.7B (3.2B active, Mamba-2 + Transformer MoE) |
| Context Length | 1,048,576 tokens (1M) |
| Weight Quantization | GGUF Q4_K_M |
| KV Cache | TurboQuant 3-bit (planar/iso) |
| File Size | ~14 GB |
| License | NVIDIA Open Model License (commercial use OK) |
| Compatible | llama.cpp, Ollama, LM Studio, koboldcpp |

## What is TurboQuant?

TurboQuant applies block-diagonal rotations (Clifford algebra) for KV cache compression. When used with llama.cpp's `--cache-type-k q4_0 --cache-type-v q4_0` flags:

| Metric | TurboQuant | TurboQuant |
|--------|-----------|-----------|
| Prefill Speed | 3,822 tok/s | 722 tok/s |
| Decode Speed | 119 tok/s | 93 tok/s |
| Perplexity | 6.91 | 7.07 |

## GGUF Quant Variants

| Quant | File Size | Quality | Variant |
|-------|-----------|---------|---------|
| Q2_K | ~9 GB | Lowest | [Q2_K](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q2_K) |
| Q3_K_M | ~11 GB | Low | [Q3_K_M](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q3_K_M) |
| IQ4_XS | ~13 GB | Medium-Low | [IQ4_XS](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-IQ4_XS) |
| **Q4_K_M** | **~14 GB** | **Medium (recommended)** | **This model** |
| Q5_K_M | ~17 GB | Medium-High | [Q5_K_M](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q5_K_M) |
| Q8_0 | ~27 GB | High | [Q8_0](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-GGUF-Q8_0) |

## See Also

- [nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) -- Base model
- [majentik/Nemotron-3-Nano-30B-A3B-TurboQuant](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant) -- TurboQuant KV-cache (transformers)
- [majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-MLX-4bit](https://huggingface.co/majentik/Nemotron-3-Nano-30B-A3B-TurboQuant-MLX-4bit) -- MLX 4-bit variant
- [TurboQuant GitHub](https://github.com/scrya-com/turboquant)