NVIDIA Nemotron 3 Nano 30B A3B Base BF16 — Hardware Requirements & GPU Compatibility
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NVIDIA Nemotron 3 Nano 30B A3B Base BF16 is the foundation model version of the Nemotron 3 Nano 30B, offered in full BF16 precision. Unlike the chat-tuned variants, this base model hasn't been instruction-tuned, making it suitable for fine-tuning, research, or custom alignment workflows. At 31.6 billion total parameters with a mixture-of-experts architecture, the base model gives developers and researchers a strong starting point for building specialized applications. It retains all the architectural benefits of the MoE design while leaving the behavioral layer open for customization.
Runs great on a 24 GB GPU
Runs on 24 GB (Q4_K_M · ~177 tok/s · 8K)Macs from 24 GB (Q2_K · ~62 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run NVIDIA Nemotron 3 Nano 30B A3B Base 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
vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX PRO 4500 (32 GB) (7 GB headroom) · Q4_K_M
- Generation speed
- ~166 tok/s
- generation speed
- Cost per 1M output tokens
- $0.57
- per 1M output tokens
How Much VRAM Does NVIDIA Nemotron 3 Nano 30B A3B Base BF16 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 14.8 GB | 22.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~209 tok/s | |
| 16.9 GB | 25 GB | 3-bit medium — 3-bit medium quantization | Fits: 16K context · ~196 tok/s | |
| 20.8 GB | 28.9 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~177 tok/s | |
| 24.8 GB | 32.8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 28.5 GB | |
| 28.7 GB | 36.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 32.4 GB | |
| 34.7 GB | 42.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 38.5 GB | |
| 69.5 GB | 77.6 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 73.2 GB |
Selected Q4_K_M
4-bit medium quantization24.6GBat 16K context
- Weights
- 20.0 GB
- KV cache
- 4.3 GB
- Overhead
- 0.3 GB
Which Hardware Can Run NVIDIA Nemotron 3 Nano 30B A3B Base BF16?
Q4_K_M · 20.8 GBNVIDIA Nemotron 3 Nano 30B A3B Base BF16 (Q4_K_M) requires 20.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 28+ GB is recommended. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 100 devices with unified memory can run NVIDIA Nemotron 3 Nano 30B A3B Base BF16, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
Q4_K_M · best fit Q2_K (18.5 GB)
1 GPU
RX 7900 XT~80Simulate output on RX 7900 XT32–40 GB
fits · 7.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~230Simulate output on RTX 5090M5 Max36 GB~64Simulate output on Mac Studio M5 Max (36 GB)M532 GB~36Simulate output on MacBook Air 13" M5 (32 GB)Orin~57Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~37Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is NVIDIA Nemotron 3 Nano 30B A3B Base BF16?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
- MMLU-Pro
TIGER-LabIndependent
Independent65.1%
#48/146 open
Alternatives to NVIDIA Nemotron 3 Nano 30B A3B Base 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 |
| Hy Memory 30B A3BTencent | — | 19.1 GB | ~184 | same size |
| Granite Switch 4.1 30BIBM | — | 22.7 GB | ~35 | 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 |
Specifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 31.6B
- Active parameters
- 3B per token
- Architecture
- NemotronHForCausalLM
- Release Date
- 2025-12-03
- License
- Other
Frequently Asked Questions
How much VRAM does NVIDIA Nemotron 3 Nano 30B A3B Base BF16 need?
NVIDIA Nemotron 3 Nano 30B A3B Base BF16 requires 20.8 GB of VRAM at Q4_K_M, or 69.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 31.6B × 4.8 bits ÷ 8 = 18.9 GB
KV Cache + Overhead ≈ 1.9 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 NVIDIA Nemotron 3 Nano 30B A3B Base BF16?
Yes, at Q4_K_M (20.8 GB) or lower. Higher quantizations like Q5_K_M (24.8 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for NVIDIA Nemotron 3 Nano 30B A3B Base BF16?
For NVIDIA Nemotron 3 Nano 30B A3B Base BF16, Q4_K_M (20.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (24.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run NVIDIA Nemotron 3 Nano 30B A3B Base BF16 on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run NVIDIA Nemotron 3 Nano 30B A3B Base BF16, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 20.8 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M1 Max (32 GB).
Can I run NVIDIA Nemotron 3 Nano 30B A3B Base BF16 locally?
Yes — NVIDIA Nemotron 3 Nano 30B A3B Base BF16 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is NVIDIA Nemotron 3 Nano 30B A3B Base BF16?
At Q4_K_M, NVIDIA Nemotron 3 Nano 30B A3B Base BF16 can reach ~100 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~177 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 → 2.0 GB active ÷ (8000 × 0.65) = 0.38 ms, plus 48 layers × 0.055 ms = 2.64 ms, so 1000 ÷ 3.02 ms = ~331 tok/s
Estimated speed at Q4_K_M (20.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of NVIDIA Nemotron 3 Nano 30B A3B Base BF16?
At Q4_K_M, the download is about 18.95 GB. The full-precision BF16 version is 63.16 GB. The smallest option (Q2_K) is 13.42 GB.
Which GPUs can run NVIDIA Nemotron 3 Nano 30B A3B Base BF16?
7 consumer GPUs can run NVIDIA Nemotron 3 Nano 30B A3B Base BF16 at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run NVIDIA Nemotron 3 Nano 30B A3B Base BF16?
100 devices with unified memory can run NVIDIA Nemotron 3 Nano 30B A3B Base BF16 at Q4_K_M (20.8 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.