NVIDIA Nemotron Nano 9B v2 Japanese — Hardware Requirements & GPU Compatibility
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NVIDIA Nemotron Nano 9B v2 Japanese is a specialized variant of the Nemotron Nano 9B v2, fine-tuned for Japanese language understanding and generation. At 8.9 billion parameters, it maintains the same hardware-friendly footprint as the English version while delivering natural Japanese conversational ability. For users looking to run a Japanese-language assistant locally, this model offers a rare combination of compact size and dedicated language optimization from a major hardware vendor. It handles Japanese text with the fluency you'd expect from a purpose-built model rather than a multilingual afterthought.
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 (IQ3_S · ~16 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run NVIDIA Nemotron Nano 9B v2 Japanese
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/tatsuyaaaaaaa/NVIDIA-Nemotron-Nano-9B-v2-Japanese-gguf:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 18.0 GB free · 16K context
Download: NVIDIA-Nemotron-Nano-9B-v2-Japanese_Q4_K_M.gguf6.53 GBtatsuyaaaaaaa
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 Japanese Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.4 GB | 4.8 GB | 3-bit · i-quant extra small — Importance-weighted 3-bit, extra small | Fits: 128K context · ~150 tok/s | |
| 4.5 GB | 4.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~146 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 Japanese?
Q4_K_M · 6.0 GBNVIDIA Nemotron Nano 9B v2 Japanese (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 Japanese, 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
Alternatives to NVIDIA Nemotron Nano 9B v2 Japanese
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 |
Specifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 8.9B
- Architecture
- NemotronHForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-02-04
- License
- Other
Frequently Asked Questions
How much VRAM does NVIDIA Nemotron Nano 9B v2 Japanese need?
NVIDIA Nemotron Nano 9B v2 Japanese 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 Japanese?
For NVIDIA Nemotron Nano 9B v2 Japanese, 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 IQ3_XS at 4.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run NVIDIA Nemotron Nano 9B v2 Japanese on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run NVIDIA Nemotron Nano 9B v2 Japanese, the smallest at IQ3_S. 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 Japanese locally?
Yes — NVIDIA Nemotron Nano 9B v2 Japanese can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is NVIDIA Nemotron Nano 9B v2 Japanese?
At Q4_K_M, NVIDIA Nemotron Nano 9B v2 Japanese 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 Japanese?
At Q4_K_M, the download is about 5.33 GB. The full-precision BF16 version is 17.78 GB. The smallest option (IQ3_XS) is 3.67 GB.
Which GPUs can run NVIDIA Nemotron Nano 9B v2 Japanese?
52 consumer GPUs can run NVIDIA Nemotron Nano 9B v2 Japanese 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 Japanese?
151 devices with unified memory can run NVIDIA Nemotron Nano 9B v2 Japanese 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.