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.
68.0K downloads
113 likesSpecifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 31.6B
- Release Date
- 2025-12-03
- License
- Other
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How Much VRAM Does NVIDIA Nemotron 3 Nano 30B A3B Base BF16 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 69.5 GB | — | 63.16 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 30B A3B Base BF16?
BF16 · 69.5 GBNVIDIA Nemotron 3 Nano 30B A3B Base BF16 (BF16) requires 69.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 91+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run NVIDIA Nemotron 3 Nano 30B A3B Base BF16?
BF16 · 69.5 GB19 devices with unified memory can run NVIDIA Nemotron 3 Nano 30B A3B Base BF16, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 96GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
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 69.5 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 31.6B × 16 bits ÷ 8 = 63.2 GB
KV Cache + Overhead ≈ 6.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1669.5 GB- Can NVIDIA GeForce RTX 5090 run NVIDIA Nemotron 3 Nano 30B A3B Base BF16?
No — NVIDIA Nemotron 3 Nano 30B A3B Base BF16 requires at least 69.5 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run NVIDIA Nemotron 3 Nano 30B A3B Base BF16 on a Mac?
NVIDIA Nemotron 3 Nano 30B A3B Base BF16 requires at least 69.5 GB at BF16, 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 30B A3B Base BF16 locally?
Yes — NVIDIA Nemotron 3 Nano 30B A3B Base BF16 can run locally on consumer hardware. At BF16 quantization it needs 69.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is NVIDIA Nemotron 3 Nano 30B A3B Base BF16?
At BF16, NVIDIA Nemotron 3 Nano 30B A3B Base BF16 can reach ~63 tok/s on AMD Instinct MI350X. 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 ÷ 69.5 × 0.65 = ~75 tok/s
Estimated speed at BF16 (69.5 GB)
~75 tok/s~75 tok/s~63 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 30B A3B Base BF16?
At BF16, the download is about 63.16 GB.
- Which GPUs can run NVIDIA Nemotron 3 Nano 30B A3B Base BF16?
No single consumer GPU has enough VRAM to run NVIDIA Nemotron 3 Nano 30B A3B Base BF16 at BF16 (69.5 GB). Multi-GPU or professional hardware is required.
- Which devices can run NVIDIA Nemotron 3 Nano 30B A3B Base BF16?
19 devices with unified memory can run NVIDIA Nemotron 3 Nano 30B A3B Base BF16 at BF16 (69.5 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), 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.