NVIDIA Nemotron Nano 9B v2 — Hardware Requirements & GPU Compatibility
ChatFunctions
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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 64K)Best on 24 GB (Q4_K_M · ~109 tok/s · 128K)Macs from 8 GB (Q2_K · ~16 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run NVIDIA Nemotron Nano 9B v2
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/bartowski/nvidia_NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 18.0 GB free · 16K context
Download: nvidia_NVIDIA-Nemotron-Nano-9B-v2-Q4_K_M.gguf6.53 GBbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (6 GB headroom) · Q4_K_M
- Generation speed
- ~98 tok/s
- generation speed
- Cost per 1M output tokens
- $0.35
- per 1M output tokens
How Much VRAM Does NVIDIA Nemotron Nano 9B v2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.5 GB | 4.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~146 tok/s | |
| 4.6 GB | 5 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~142 tok/s | |
| 5.0 GB | 5.5 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~130 tok/s | |
| 5.2 GB | 5.6 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 128K context · ~127 tok/s | |
| 6.0 GB | 6.5 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~109 tok/s | |
| 7.0 GB | 7.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~93 tok/s | |
| 8.0 GB | 8.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~82 tok/s | |
| 9.6 GB | 10 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~68 tok/s |
Selected Q4_K_M
4-bit medium quantization6.0GBat 16K context
- Weights
- 5.3 GB
- KV cache
- 0.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 18.0 GB free · ~109 tok/s
≥50 tok/s needs ≥464 GB/s · RTX 5090 ~193, RTX 3090 Ti ~109
Which Hardware 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 2.9 GB, bringing total usage to 8.9 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run NVIDIA Nemotron Nano 9B v2, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 2.0 GB free · up to 64K ctx
16 GPUs · 1 device family
RTX 3080~82Simulate output on RTX 3080RTX 2080 Ti~66Simulate output on RTX 2080 TiRTX 3070 Ti~66Simulate output on RTX 3070 TiOrin Nano~11Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 14.0 GB free · up to 128K ctx
1 GPU
RX 7900 XT~80Simulate output on RX 7900 XT32–40 GB
fits · 26.0 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~193Simulate output on RTX 5090M5 Max36 GB~53Simulate output on Mac Studio M5 Max (36 GB)M532 GB~18Simulate output on MacBook Air 13" M5 (32 GB)Orin~22Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~15Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is NVIDIA Nemotron Nano 9B v2?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 2 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- CritPt
Epoch AI · Aug 2025Lab-reported
Lab-reported0.0%
#76/84 open
- Fiction.LiveBench
Epoch AI · Aug 2025Independent
Independent25.0%
#25/25 open
Alternatives to NVIDIA Nemotron Nano 9B v2
Nemotron family · 45 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| Ornith 1.0 9BDeep Reinforce | — | 6.5 GB | ~106 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
| MiniCPM O 4 5OpenBMB | — | 8.3 GB | ~105 | same size |
Fine-tunes & variants(2)
Specifications
- 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
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 2.9 GB (8.9 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)
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 ≈ 3.6 GB (at full 131K context)
VRAM usage by quantization
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. Q4_K_L (6.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_S at 3.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run NVIDIA Nemotron Nano 9B v2 on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run NVIDIA Nemotron Nano 9B v2, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 6.0 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run NVIDIA Nemotron Nano 9B v2 locally?
Yes — NVIDIA Nemotron Nano 9B v2 can run locally on consumer hardware. 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 ~795 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 ÷ 6.04 × 0.65 = ~861 tok/s
Estimated speed at Q4_K_M (6.0 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 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 (IQ2_S) is 2.78 GB.
Which GPUs can run NVIDIA Nemotron Nano 9B v2?
52 consumer GPUs can run NVIDIA Nemotron Nano 9B v2 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run NVIDIA Nemotron Nano 9B v2?
151 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, 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.