Nemotron 3 Nano Omni 30B A3B Reasoning BF16 — Hardware Requirements & GPU Compatibility
Reasoning
Nemotron 3 Nano Omni 30B A3B Reasoning BF16 is a 33.0B-parameter open language model from NVIDIA in the Nemotron family. At Q4_K_M it needs about 21.79 GB of VRAM — see which GPUs and Macs can run it below.
Runs well on a 24 GB GPU
Runs from 16 GB (IQ2_XXS · 16K)Best on 24 GB (Q4_K_M · ~130 tok/s · 8K)Macs from 18 GB (IQ2_XXS · ~42 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Nemotron 3 Nano Omni 30B A3B Reasoning BF16
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/unsloth/NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF:Q4_K_MQ4_K_M · needs ~25.6 GB · 16K context
Download: NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-UD-Q4_K_M.gguf23.89 GBunsloth
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 32 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX PRO 4500 (32 GB) (6 GB headroom) · Q4_K_M
- Generation speed
- ~120 tok/s
- generation speed
- Cost per 1M output tokens
- $0.79
- per 1M output tokens
How Much VRAM Does Nemotron 3 Nano Omni 30B A3B Reasoning BF16 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 10.0 GB | 18.2 GB | 2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss | Fits: 32K context · ~202 tok/s | |
| 12.3 GB | 20.4 GB | 2-bit · i-quant medium — Importance-weighted 2-bit, medium | Fits: 32K context · ~182 tok/s | |
| 15.4 GB | 23.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~161 tok/s | |
| 17.7 GB | 25.9 GB | 3-bit medium — 3-bit medium quantization | Fits: 16K context · ~148 tok/s | |
| 21.8 GB | 30 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~130 tok/s | |
| 25.9 GB | 34.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 29.7 GB | |
| 30.0 GB | 38.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 33.8 GB | |
| 36.3 GB | 44.5 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 40.1 GB |
Selected Q4_K_M
4-bit medium quantization25.6GBat 16K context
- Weights
- 20.9 GB
- KV cache
- 4.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · fits up to 8K context · ~130 tok/s
Or .
Which Hardware Can Run Nemotron 3 Nano Omni 30B A3B Reasoning BF16?
Q4_K_M · 21.8 GBNemotron 3 Nano Omni 30B A3B Reasoning BF16 (Q4_K_M) requires 21.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 29+ GB is recommended. 7 GPUs can run it, including NVIDIA GeForce RTX 5090. 100 devices with unified memory can run Nemotron 3 Nano Omni 30B A3B Reasoning BF16, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M5 Max (36 GB).
20 GB
Q4_K_M · best fit Q2_K (19.3 GB)
1 GPU
RX 7900 XT~69Simulate output on RX 7900 XT32–40 GB
fits · 6.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~182Simulate output on RTX 5090M5 Max36 GB~50Simulate output on Mac Studio M5 Max (36 GB)M532 GB~25Simulate output on MacBook Air 13" M5 (32 GB)Orin~36Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~24Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Nemotron 3 Nano Omni 30B A3B Reasoning BF16
Nemotron family · 45 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Nanbeige4.3 33BNanbeige | — | 23.7 GB | ~32 | same size |
| MiniCPM V 4.7 35B A3BOpenBMB | — | 22.1 GB | ~169 | same size |
| Llm Jp 4.1 32B A3b Thinkingllm-jp | — | 20.1 GB | ~188 | same size |
| Llm Jp 4.1 33B Thinkingllm-jp | — | 22.5 GB | ~35 | same size |
| Agnes 3.0 FlashAgnes-AI | — | 22.1 GB | ~35 | same size |
| Agents A1InternScience | — | 21.5 GB | ~171 | same size |
Specifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 33.0B
- Active parameters
- 5B per token
- Architecture
- NemotronH_Nano_Omni_Reasoning_V3
- Release Date
- 2026-04-20
- License
- Other
Frequently Asked Questions
How much VRAM does Nemotron 3 Nano Omni 30B A3B Reasoning BF16 need?
Nemotron 3 Nano Omni 30B A3B Reasoning BF16 requires 21.8 GB of VRAM at Q4_K_M, or 72.6 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 33.0B × 4.8 bits ÷ 8 = 19.8 GB
KV Cache + Overhead ≈ 2 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Nemotron 3 Nano Omni 30B A3B Reasoning BF16?
Yes, at Q4_K_M (21.8 GB) or lower. Higher quantizations like Q5_K_S (25.0 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Nemotron 3 Nano Omni 30B A3B Reasoning BF16?
For Nemotron 3 Nano Omni 30B A3B Reasoning BF16, Q4_K_M (21.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (25.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 10.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Nemotron 3 Nano Omni 30B A3B Reasoning BF16 on a Mac?
Yes — MacBook Pro 14" M3 Pro (18 GB) and 108 other Macs can run Nemotron 3 Nano Omni 30B A3B Reasoning BF16, the smallest at IQ2_XXS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 21.8 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M1 Max (32 GB).
Can I run Nemotron 3 Nano Omni 30B A3B Reasoning BF16 locally?
Yes — Nemotron 3 Nano Omni 30B A3B Reasoning BF16 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Nemotron 3 Nano Omni 30B A3B Reasoning BF16?
At Q4_K_M, Nemotron 3 Nano Omni 30B A3B Reasoning BF16 can reach ~97 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~130 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 3.3 GB active ÷ (8000 × 0.65) = 0.64 ms, plus 48 layers × 0.055 ms = 2.64 ms, so 1000 ÷ 3.28 ms = ~305 tok/s
Estimated speed at Q4_K_M (21.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Nemotron 3 Nano Omni 30B A3B Reasoning BF16?
At Q4_K_M, the download is about 19.81 GB. The full-precision BF16 version is 66.03 GB. The smallest option (IQ2_XXS) is 9.08 GB.
Which GPUs can run Nemotron 3 Nano Omni 30B A3B Reasoning BF16?
7 consumer GPUs can run Nemotron 3 Nano Omni 30B A3B Reasoning BF16 at Q4_K_M. Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090.
Which devices can run Nemotron 3 Nano Omni 30B A3B Reasoning BF16?
100 devices with unified memory can run Nemotron 3 Nano Omni 30B A3B Reasoning BF16 at Q4_K_M (21.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.