NVIDIA Nemotron Nano 12B v2 — Hardware Requirements & GPU Compatibility
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NVIDIA Nemotron Nano 12B v2 is a 12B-parameter open language model from NVIDIA in the Nemotron family. At BF16 it needs about 26.40 GB of VRAM — see which GPUs and Macs can run it below.
19.8K downloads
156 likesSpecifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 12B
- Release Date
- 2025-08-21
- License
- Other
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HuggingFace
How Much VRAM Does NVIDIA Nemotron Nano 12B v2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 26.4 GB | — | 24.00 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 Nano 12B v2?
BF16 · 26.4 GBNVIDIA Nemotron Nano 12B v2 (BF16) requires 26.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 35+ GB is recommended. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run NVIDIA Nemotron Nano 12B v2?
BF16 · 26.4 GB31 devices with unified memory can run NVIDIA Nemotron Nano 12B v2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does NVIDIA Nemotron Nano 12B v2 need?
NVIDIA Nemotron Nano 12B v2 requires 26.4 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 12B × 16 bits ÷ 8 = 24 GB
KV Cache + Overhead ≈ 2.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1626.4 GB- Can I run NVIDIA Nemotron Nano 12B v2 on a Mac?
NVIDIA Nemotron Nano 12B v2 requires at least 26.4 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 Nano 12B v2 locally?
Yes — NVIDIA Nemotron Nano 12B v2 can run locally on consumer hardware. At BF16 quantization it needs 26.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is NVIDIA Nemotron Nano 12B v2?
At BF16, NVIDIA Nemotron Nano 12B v2 can reach ~167 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 ÷ 26.4 × 0.65 = ~197 tok/s
Estimated speed at BF16 (26.4 GB)
~197 tok/s~197 tok/s~167 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 Nano 12B v2?
At BF16, the download is about 24.00 GB.
- Which GPUs can run NVIDIA Nemotron Nano 12B v2?
1 consumer GPU can run NVIDIA Nemotron Nano 12B v2 at BF16 (26.4 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run NVIDIA Nemotron Nano 12B v2?
35 devices with unified memory can run NVIDIA Nemotron Nano 12B v2 at BF16 (26.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.