docs: Tier 2 polish — variant matrix + quant trade-off

087a85e verified 1 day ago

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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, rotorquant, 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 - RotorQuant MLX 8-bit + RotorQuant KV-Cache (matched stack)

	Documentation card for the matched RotorQuant weight + RotorQuant KV-cache stack
	of `Nemotron-3-Nano-Omni-30B-A3B-Reasoning` at MLX 8-bit.

	No new weights are published here. Load the weights from
	[`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-8bit`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-8bit)
	and apply the RotorQuant KV-cache modifier documented in
	[`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant).

	## Quickstart

	This card pairs the RotorQuant weights with the RotorQuant 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-RotorQuant-MLX-8bit-RQ-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 — `RotorQuant-MLX-8bit-RQ-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 \| 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](https://huggingface.co/majentik/nemotron3-nano-omni-30b-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 \|

	---
	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, rotorquant, 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 - RotorQuant MLX 8-bit + RotorQuant KV-Cache (matched stack)

	Documentation card for the matched RotorQuant weight + RotorQuant KV-cache stack
	of `Nemotron-3-Nano-Omni-30B-A3B-Reasoning` at MLX 8-bit.

	No new weights are published here. Load the weights from
	[`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-8bit`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-MLX-8bit)
	and apply the RotorQuant KV-cache modifier documented in
	[`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant).

	## Quickstart

	This card pairs the RotorQuant weights with the RotorQuant 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-RotorQuant-MLX-8bit-RQ-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 — `RotorQuant-MLX-8bit-RQ-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 \| 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](https://huggingface.co/majentik/nemotron3-nano-omni-30b-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 \|