NVIDIA Nemotron 3 Nano 4B BF16 — Hardware Requirements & GPU Compatibility
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NVIDIA Nemotron 3 Nano 4B BF16 is a 4.0B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 2.90 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 256K)Best on 24 GB (Q4_K_M · ~226 tok/s · 256K)Macs from 8 GB (Q4_K_M · ~25 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run NVIDIA Nemotron 3 Nano 4B BF16
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/lmstudio-community/NVIDIA-Nemotron-3-Nano-4B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 21.1 GB free · 16K context
Download: NVIDIA-Nemotron-3-Nano-4B-Q4_K_M.gguf2.84 GBlmstudio-community
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (9 GB headroom) · Q4_K_M
- Generation speed
- ~81 tok/s
- generation speed
- Cost per 1M output tokens
- $0.24
- per 1M output tokens
How Much VRAM Does NVIDIA Nemotron 3 Nano 4B BF16 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.2 GB | 2.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~298 tok/s | |
| 2.3 GB | 2.5 GB | 3-bit small — 3-bit small quantization | Fits: 256K context · ~291 tok/s | |
| 2.5 GB | 2.7 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~267 tok/s | |
| 2.5 GB | 2.8 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 256K context · ~262 tok/s | |
| 2.9 GB | 3.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~226 tok/s | |
| 3.4 GB | 3.6 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~196 tok/s | |
| 3.8 GB | 4.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~173 tok/s | |
| 4.5 GB | 4.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~146 tok/s |
Selected Q4_K_M
4-bit medium quantization2.9GBat 16K context
- Weights
- 2.4 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.1 GB free · ~226 tok/s
≥50 tok/s needs ≥223 GB/s · RTX 5090 ~402, RTX 3090 Ti ~226
Which Hardware Can Run NVIDIA Nemotron 3 Nano 4B BF16?
Q4_K_M · 2.9 GBNVIDIA Nemotron 3 Nano 4B BF16 (Q4_K_M) requires 2.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 262K context window can add up to 3.7 GB, bringing total usage to 6.6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run NVIDIA Nemotron 3 Nano 4B BF16, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.1 GB free · up to 256K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~170Simulate output on RTX 3080RTX 2080 Ti~138Simulate output on RTX 2080 TiRTX 3070 Ti~136Simulate output on RTX 3070 TiM3*8 GB~25Simulate output on iMac 24" M3 (8 GB)Orin Nano~23Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~17Simulate output on iPhone 1720 GB
fits · 17.1 GB free · up to 256K ctx
1 GPU
RX 7900 XT~166Simulate output on RX 7900 XT32–40 GB
fits · 29.1 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~402Simulate output on RTX 5090M5 Max36 GB~111Simulate output on Mac Studio M5 Max (36 GB)M532 GB~37Simulate output on MacBook Air 13" M5 (32 GB)Orin~46Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~30Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to NVIDIA Nemotron 3 Nano 4B BF16
Nemotron family · 42 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Vision 4.1 4BIBM | — | 4 GB | ~228 | same size |
| Qwen3 4B Instruct 2507Alibaba | — | 4.2 GB | ~226 | same size |
| MiniCPM V 4OpenBMB | — | 3.1 GB | ~236 | same size |
| Phi 4 Mini ReasoningMicrosoft | — | 4.8 GB | ~228 | same size |
| Qwen2.5 VL 3B InstructAlibaba | — | 3.2 GB | ~249 | same size |
| Phi 3.5 Vision InstructMicrosoft | — | 9.2 GB | ~183 | same size |
Specifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 4.0B
- Architecture
- NemotronHForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-03-07
- License
- Other
Frequently Asked Questions
How much VRAM does NVIDIA Nemotron 3 Nano 4B BF16 need?
NVIDIA Nemotron 3 Nano 4B BF16 requires 2.9 GB of VRAM at Q4_K_M, or 8.5 GB at BF16. Full 262K context adds up to 3.7 GB (6.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.0B × 4.8 bits ÷ 8 = 2.4 GB
KV Cache + Overhead ≈ 0.5 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.
KV Cache + Overhead ≈ 4.2 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for NVIDIA Nemotron 3 Nano 4B BF16?
For NVIDIA Nemotron 3 Nano 4B BF16, Q4_K_M (2.9 GB) offers the best balance of quality and VRAM usage. Q4_K_L (3.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run NVIDIA Nemotron 3 Nano 4B BF16 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run NVIDIA Nemotron 3 Nano 4B BF16. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run NVIDIA Nemotron 3 Nano 4B BF16 locally?
Yes — NVIDIA Nemotron 3 Nano 4B BF16 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is NVIDIA Nemotron 3 Nano 4B BF16?
At Q4_K_M, NVIDIA Nemotron 3 Nano 4B BF16 can reach ~1655 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~226 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 2.9 × 0.65 = ~1793 tok/s
Estimated speed at Q4_K_M (2.9 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 4B BF16?
At Q4_K_M, the download is about 2.38 GB. The full-precision BF16 version is 7.95 GB. The smallest option (IQ2_XXS) is 1.09 GB.
Which GPUs can run NVIDIA Nemotron 3 Nano 4B BF16?
52 consumer GPUs can run NVIDIA Nemotron 3 Nano 4B BF16 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run NVIDIA Nemotron 3 Nano 4B BF16?
163 devices with unified memory can run NVIDIA Nemotron 3 Nano 4B BF16 at Q4_K_M (2.9 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.