NVIDIA Nemotron Nano 9B v2 — Hardware Requirements & GPU Compatibility
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NVIDIA Nemotron Nano 9B v2 is a compact yet capable chat model from NVIDIA, packing 8.9 billion parameters into a size that runs comfortably on a wide range of consumer GPUs. Built on NVIDIA's Nemotron architecture, it delivers strong instruction-following and conversational performance while keeping VRAM requirements modest. This second-generation Nano model reflects NVIDIA's push to make high-quality language models accessible on local hardware. It's an excellent starting point for users who want a responsive, general-purpose assistant without needing top-tier GPU memory.
308.0K downloads
482 likesSpecifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 8.9B
- Architecture
- NemotronHForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2025-08-12
- License
- Other
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HuggingFace
How Much VRAM Does NVIDIA Nemotron Nano 9B v2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.5 GB | 30.4 GB | 3.78 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.6 GB | 30.5 GB | 3.89 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 5.0 GB | 30.9 GB | 4.33 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 5.2 GB | 31.1 GB | 4.44 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 6.0 GB | 31.9 GB | 5.33 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 7.0 GB | 32.9 GB | 6.33 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 8.0 GB | 33.9 GB | 7.33 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 9.6 GB | 35.5 GB | 8.89 GB | 8-bit quantization, near-lossless |
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 9B v2?
Q4_K_M · 6.0 GBNVIDIA Nemotron Nano 9B v2 (Q4_K_M) requires 6.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 131K context window can add up to 25.9 GB, bringing total usage to 31.9 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run NVIDIA Nemotron Nano 9B v2?
Q4_K_M · 6.0 GB58 devices with unified memory can run NVIDIA Nemotron Nano 9B v2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download NVIDIA Nemotron Nano 9B v2
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does NVIDIA Nemotron Nano 9B v2 need?
NVIDIA Nemotron Nano 9B v2 requires 6.0 GB of VRAM at Q4_K_M, or 18.5 GB at BF16. Full 131K context adds up to 25.9 GB (31.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.9B × 4.8 bits ÷ 8 = 5.3 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 26.6 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M6.0 GBQ4_K_M + full context31.9 GB- What's the best quantization for NVIDIA Nemotron Nano 9B v2?
For NVIDIA Nemotron Nano 9B v2, Q4_K_M (6.0 GB) offers the best balance of quality and VRAM usage. Q5_0 (6.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.5 GB.
VRAM requirement by quantization
Q2_K4.5 GBQ4_05.2 GBQ4_15.7 GBQ4_K_M ★6.0 GBQ5_K_S6.8 GBBF1618.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run NVIDIA Nemotron Nano 9B v2 on a Mac?
NVIDIA Nemotron Nano 9B v2 requires at least 4.5 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 Nano 9B v2 locally?
Yes — NVIDIA Nemotron Nano 9B v2 can run locally on consumer hardware. At Q4_K_M quantization it needs 6.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is NVIDIA Nemotron Nano 9B v2?
At Q4_K_M, NVIDIA Nemotron Nano 9B v2 can reach ~729 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~109 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 ÷ 6.0 × 0.65 = ~861 tok/s
Estimated speed at Q4_K_M (6.0 GB)
~861 tok/s~109 tok/s~861 tok/s~729 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 9B v2?
At Q4_K_M, the download is about 5.33 GB. The full-precision BF16 version is 17.78 GB. The smallest option (Q2_K) is 3.78 GB.
- Which GPUs can run NVIDIA Nemotron Nano 9B v2?
50 consumer GPUs can run NVIDIA Nemotron Nano 9B v2 at Q4_K_M (6.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run NVIDIA Nemotron Nano 9B v2?
59 devices with unified memory can run NVIDIA Nemotron Nano 9B v2 at Q4_K_M (6.0 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.