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.
342.4K downloads
90 likesSpecifications
- 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
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HuggingFace
How Much VRAM Does NVIDIA Nemotron 3 Nano 4B BF16 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 2.2 GB | 29.6 GB | 1.69 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 2.5 GB | 29.9 GB | 1.94 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 2.9 GB | 30.3 GB | 2.38 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 3.4 GB | 30.8 GB | 2.83 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 3.8 GB | 31.2 GB | 3.28 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 4.5 GB | 31.9 GB | 3.97 GB | 8-bit quantization, near-lossless |
| BF16 | 16.00 | 8.5 GB | 35.9 GB | 7.95 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs 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 27.4 GB, bringing total usage to 30.3 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run NVIDIA Nemotron 3 Nano 4B BF16?
Q4_K_M · 2.9 GB59 devices with unified memory can run NVIDIA Nemotron 3 Nano 4B BF16, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download NVIDIA Nemotron 3 Nano 4B BF16
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
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 27.4 GB (30.3 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)
KV Cache + Overhead ≈ 27.9 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M2.9 GBQ4_K_M + full context30.3 GB- 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. Q5_K_M (3.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.2 GB.
VRAM requirement by quantization
Q2_K2.2 GBQ4_K_M ★2.9 GBQ5_K_M3.4 GBQ6_K3.8 GBQ8_04.5 GBBF168.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run NVIDIA Nemotron 3 Nano 4B BF16 on a Mac?
NVIDIA Nemotron 3 Nano 4B BF16 requires at least 2.2 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run NVIDIA Nemotron 3 Nano 4B BF16 locally?
Yes — NVIDIA Nemotron 3 Nano 4B BF16 can run locally on consumer hardware. At Q4_K_M quantization it needs 2.9 GB of VRAM. 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 ~1517 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)
~1793 tok/s~226 tok/s~1793 tok/s~1517 tok/sReal-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 (Q2_K) is 1.69 GB.
- Which GPUs can run NVIDIA Nemotron 3 Nano 4B BF16?
50 consumer GPUs can run NVIDIA Nemotron 3 Nano 4B BF16 at Q4_K_M (2.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run NVIDIA Nemotron 3 Nano 4B BF16?
59 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, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.