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Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-5bit-TQ-KV

Text Generation
MLX
English
nemotron
multimodal
mamba2
Mixture of Experts
quantized
turboquant
kv-cache-modifier
apple-silicon
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matched-stack
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Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-5bit-TQ-KV
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---
license: other
license_name: nvidia-open-model-license
license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
base_model: nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
tags: [nemotron, multimodal, mamba2, moe, quantized, turboquant, mlx, kv-cache-modifier,
  apple-silicon, runtime-modifier, matched-stack]
library_name: mlx
pipeline_tag: text-generation
language: [en]
datasets: [nvidia/Nemotron-Image-Training-v3]
inference: false
---

# Nemotron-3-Nano-Omni-30B-A3B-Reasoning - TurboQuant MLX 5-bit + TurboQuant KV-Cache (matched stack)

Documentation card for the matched TurboQuant weight + TurboQuant KV-cache stack
of `Nemotron-3-Nano-Omni-30B-A3B-Reasoning` at MLX 5-bit.

**No new weights are published here.** Load the weights from
[`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-5bit`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-5bit)
and apply the TurboQuant KV-cache modifier documented in
[`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant).

## Quickstart

This card pairs the TurboQuant weights with the TurboQuant KV-cache modifier (matched stack). Both are documentation-only — load the parent weight repo for actual MLX shards.
```python
# Today (mlx-lm 0.31.x): the NemotronH_Nano_Omni_Reasoning_V3 model class
# is not yet registered in mlx-lm. The cell below is the API shape that WILL
# work once upstream lands the class (track ml-explore/mlx-lm#386).

from mlx_lm import load, generate

model, tokenizer = load("majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-5bit-TQ-KV")

prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Solve: 17 * 23"}],
    add_generation_prompt=True,
    enable_thinking=False,  # set True to enable extended reasoning (default)
)

response = generate(
    model, tokenizer,
    prompt=prompt,
    max_tokens=512,
    sampler=lambda x: x.argmax(axis=-1),  # or use mlx_lm.sample_utils.make_sampler(temp=0.6, top_p=0.95)
)
print(response)
```

> ⚠️ This variant covers the **text tower only**. For multimodal inference (vision + audio + video), use the GGUF variants with `llama-mtmd-cli` — see the GGUF cards in this family.

## Modality matrix

| Modality | Encoder | Quantization in this variant |
|---|---|---|
| Text | LLM backbone (Mamba-2 + Transformer hybrid Sparse MoE) | per the variant suffix |
| Image | CRADIO v4-H | **BF16** (kept full-precision in every non-GGUF variant; GGUF uses mmproj-F16 split file) |
| Audio | Parakeet-TDT-0.6B-v2 | **BF16** (same rationale) |
| Video | Parakeet-TDT-0.6B-v2 + frame sampler | **BF16** (≤ 2 min, 256 frames @ 2 FPS) |

NVIDIA's official FP8 / NVFP4 recipe keeps both encoders + the cross-modal
MLP projectors in BF16 to preserve multimodal accuracy. We follow that
convention in every quantized variant we ship.

## Runtime quirks

### MLX-LM (text-only)

This variant covers the LLM backbone only. Vision + audio encoders
are NOT included — MLX-VLM Nemotron-Omni model class is
**pending upstream support** (no PR observed as of 2026-05-04).

Use the `mlx_lm.generate` API; `enable_thinking` is a runtime flag
(see below).

### Reasoning mode

`enable_thinking` defaults to `True`. To disable extended reasoning
(e.g., for latency-sensitive cases), pass `enable_thinking=False`
to the chat template / generate call. No separate "no-think"
variant card exists — this is a runtime flag, not a model variant.

## Variants in this family

(Showing 56 sibling variants under `majentik/nemotron3-nano-omni-30b-*`. The current variant — `TurboQuant-MLX-5bit-TQ-KV` — is **bolded**.)

| Variant | Runtime | Approx size | Use case |
|---|---|---|---|
| [mmproj-F16](https://huggingface.co/majentik/nemotron3-nano-omni-30b-mmproj-f16) | llama-mtmd-cli | ~1-2 GB | Multimodal projector (pair with any GGUF) |
| [RotorQuant](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant) | runtime modifier | n/a | KV-cache root (weight-agnostic) |
| [RotorQuant-GGUF-IQ4_XS](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-IQ4_XS) | llama.cpp | ~26 GB | Lossy 4-bit, low-RAM CPU/edge |
| [RotorQuant-GGUF-MXFP4_MOE](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-MXFP4_MOE) | llama.cpp | ~30 GB | MXFP4 MoE quant |
| [RotorQuant-GGUF-Q2_K](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q2_K) | llama.cpp | ~18 GB | Lossy, low-RAM CPU/edge |
| [RotorQuant-GGUF-Q3_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q3_K_M) | llama.cpp | ~23 GB | Smaller 3-bit, CPU-friendly |
| [RotorQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q4_K_M) | llama.cpp | ~33 GB | Balanced default |
| [RotorQuant-GGUF-Q5_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q5_K_M) | llama.cpp | ~40 GB | Higher fidelity, more RAM |
| [RotorQuant-GGUF-Q8_0](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q8_0) | llama.cpp | ~63 GB | Near-lossless reference |
| [RotorQuant-GGUF-IQ4_XS-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-iq4_xs-rq-kv) | llama.cpp | ~26 GB | IQ4_XS + RotorQuant KV |
| [RotorQuant-GGUF-MXFP4_MOE-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-mxfp4_moe-rq-kv) | llama.cpp | ~30 GB | MXFP4 MoE + RotorQuant KV |
| [RotorQuant-GGUF-Q2_K-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q2_k-rq-kv) | llama.cpp | ~18 GB | Q2_K + RotorQuant KV |
| [RotorQuant-GGUF-Q3_K_M-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q3_k_m-rq-kv) | llama.cpp | ~23 GB | Q3_K_M + RotorQuant KV |
| [RotorQuant-GGUF-Q4_K_M-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q4_k_m-rq-kv) | llama.cpp | ~33 GB | Q4_K_M + RotorQuant KV |
| [RotorQuant-GGUF-Q5_K_M-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q5_k_m-rq-kv) | llama.cpp | ~40 GB | Q5_K_M + RotorQuant KV |
| [RotorQuant-GGUF-Q8_0-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q8_0-rq-kv) | llama.cpp | ~63 GB | Q8_0 + RotorQuant KV |
| [RotorQuant-MLX-2bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-2bit) | mlx-lm | ~9.6 GB | Apple Silicon, smallest |
| [RotorQuant-MLX-2bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-2bit-rq-kv) | mlx-lm | ~9.6 GB | 2-bit + RotorQuant KV |
| [RotorQuant-MLX-3bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-3bit) | mlx-lm | ~14 GB | Apple Silicon, small |
| [RotorQuant-MLX-3bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-3bit-rq-kv) | mlx-lm | ~14 GB | 3-bit + RotorQuant KV |
| [RotorQuant-MLX-4bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-4bit) | mlx-lm | ~19 GB | Apple Silicon balanced |
| [RotorQuant-MLX-4bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-4bit-rq-kv) | mlx-lm | ~19 GB | 4-bit + RotorQuant KV |
| [RotorQuant-MLX-5bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-5bit) | mlx-lm | ~23 GB | Apple Silicon, higher fidelity |
| [RotorQuant-MLX-5bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-5bit-rq-kv) | mlx-lm | ~23 GB | 5-bit + RotorQuant KV |
| [RotorQuant-MLX-6bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-6bit) | mlx-lm | ~27 GB | Apple Silicon, near-lossless |
| [RotorQuant-MLX-6bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-6bit-rq-kv) | mlx-lm | ~27 GB | 6-bit + RotorQuant KV |
| [RotorQuant-MLX-8bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-8bit) | mlx-lm | ~35 GB | Apple Silicon reference |
| [RotorQuant-MLX-8bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-8bit-rq-kv) | mlx-lm | ~35 GB | 8-bit + RotorQuant KV |
| [RotorQuant-MLX-MXFP4](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-mxfp4) | mlx-lm | ~19 GB | Apple Silicon MXFP4 |
| [TurboQuant](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant) | runtime modifier | n/a | KV-cache root (weight-agnostic) |
| [TurboQuant-GGUF-IQ4_XS](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-IQ4_XS) | llama.cpp | ~26 GB | Lossy 4-bit, low-RAM CPU/edge |
| [TurboQuant-GGUF-MXFP4_MOE](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-MXFP4_MOE) | llama.cpp | ~30 GB | MXFP4 MoE quant |
| [TurboQuant-GGUF-Q2_K](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q2_K) | llama.cpp | ~18 GB | Lossy, low-RAM CPU/edge |
| [TurboQuant-GGUF-Q3_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q3_K_M) | llama.cpp | ~23 GB | Smaller 3-bit, CPU-friendly |
| [TurboQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q4_K_M) | llama.cpp | ~33 GB | Balanced default |
| [TurboQuant-GGUF-Q5_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q5_K_M) | llama.cpp | ~40 GB | Higher fidelity, more RAM |
| [TurboQuant-GGUF-Q8_0](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q8_0) | llama.cpp | ~63 GB | Near-lossless reference |
| [TurboQuant-GGUF-IQ4_XS-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-iq4_xs-tq-kv) | llama.cpp | ~26 GB | IQ4_XS + TurboQuant KV |
| [TurboQuant-GGUF-MXFP4_MOE-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-mxfp4_moe-tq-kv) | llama.cpp | ~30 GB | MXFP4 MoE + TurboQuant KV |
| [TurboQuant-GGUF-Q2_K-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q2_k-tq-kv) | llama.cpp | ~18 GB | Q2_K + TurboQuant KV |
| [TurboQuant-GGUF-Q3_K_M-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q3_k_m-tq-kv) | llama.cpp | ~23 GB | Q3_K_M + TurboQuant KV |
| [TurboQuant-GGUF-Q4_K_M-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q4_k_m-tq-kv) | llama.cpp | ~33 GB | Q4_K_M + TurboQuant KV |
| [TurboQuant-GGUF-Q5_K_M-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q5_k_m-tq-kv) | llama.cpp | ~40 GB | Q5_K_M + TurboQuant KV |
| [TurboQuant-GGUF-Q8_0-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q8_0-tq-kv) | llama.cpp | ~63 GB | Q8_0 + TurboQuant KV |
| [TurboQuant-MLX-2bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-2bit) | mlx-lm | ~9.6 GB | Apple Silicon, smallest |
| [TurboQuant-MLX-2bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-2bit-tq-kv) | mlx-lm | ~9.6 GB | 2-bit + TurboQuant KV |
| [TurboQuant-MLX-3bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-3bit) | mlx-lm | ~14 GB | Apple Silicon, small |
| [TurboQuant-MLX-3bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-3bit-tq-kv) | mlx-lm | ~14 GB | 3-bit + TurboQuant KV |
| [TurboQuant-MLX-4bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-4bit) | mlx-lm | ~19 GB | Apple Silicon balanced |
| [TurboQuant-MLX-4bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-4bit-tq-kv) | mlx-lm | ~19 GB | 4-bit + TurboQuant KV |
| [TurboQuant-MLX-5bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-5bit) | mlx-lm | ~23 GB | Apple Silicon, higher fidelity |
| **TurboQuant-MLX-5bit-TQ-KV** | mlx-lm | ~23 GB | 5-bit + TurboQuant KV |
| [TurboQuant-MLX-6bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-6bit) | mlx-lm | ~27 GB | Apple Silicon, near-lossless |
| [TurboQuant-MLX-6bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-6bit-tq-kv) | mlx-lm | ~27 GB | 6-bit + TurboQuant KV |
| [TurboQuant-MLX-8bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-8bit) | mlx-lm | ~35 GB | Apple Silicon reference |
| [TurboQuant-MLX-8bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-8bit-tq-kv) | mlx-lm | ~35 GB | 8-bit + TurboQuant KV |