NVIDIA Nemotron Nano 9B v2 GGUF — Hardware Requirements & GPU Compatibility
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HuggingFace
How Much VRAM Does NVIDIA Nemotron Nano 9B v2 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.2 GB | — | 3.83 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.3 GB | — | 3.94 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.8 GB | — | 4.39 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 5.0 GB | — | 4.50 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.9 GB | — | 5.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 7.0 GB | — | 6.41 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 8.2 GB | — | 7.42 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 9.9 GB | — | 9.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run NVIDIA Nemotron Nano 9B v2 GGUF?
Q4_K_M · 5.9 GBNVIDIA Nemotron Nano 9B v2 GGUF (Q4_K_M) requires 5.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run NVIDIA Nemotron Nano 9B v2 GGUF?
Q4_K_M · 5.9 GB33 devices with unified memory can run NVIDIA Nemotron Nano 9B v2 GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does NVIDIA Nemotron Nano 9B v2 GGUF need?
NVIDIA Nemotron Nano 9B v2 GGUF requires 5.9 GB of VRAM at Q4_K_M, or 9.9 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 9B × 4.8 bits ÷ 8 = 5.4 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M5.9 GB- What's the best quantization for NVIDIA Nemotron Nano 9B v2 GGUF?
For NVIDIA Nemotron Nano 9B v2 GGUF, Q4_K_M (5.9 GB) offers the best balance of quality and VRAM usage. Q5_0 (6.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.2 GB.
VRAM requirement by quantization
Q2_K4.2 GB~75%Q4_05.0 GB~85%Q4_K_M ★5.9 GB~89%Q5_06.2 GB~90%Q5_K_S6.8 GB~92%Q8_09.9 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run NVIDIA Nemotron Nano 9B v2 GGUF on a Mac?
NVIDIA Nemotron Nano 9B v2 GGUF requires at least 4.2 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 GGUF locally?
Yes — NVIDIA Nemotron Nano 9B v2 GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 5.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is NVIDIA Nemotron Nano 9B v2 GGUF?
At Q4_K_M, NVIDIA Nemotron Nano 9B v2 GGUF can reach ~491 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~110 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 5.9 × 0.55 = ~491 tok/s
Estimated speed at Q4_K_M (5.9 GB)
AMD Instinct MI300X~491 tok/sNVIDIA GeForce RTX 4090~110 tok/sNVIDIA H100 SXM~367 tok/sAMD Instinct MI250X~303 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 GGUF?
At Q4_K_M, the download is about 5.40 GB. The full-precision Q8_0 version is 9.00 GB. The smallest option (Q2_K) is 3.83 GB.
- Which GPUs can run NVIDIA Nemotron Nano 9B v2 GGUF?
35 consumer GPUs can run NVIDIA Nemotron Nano 9B v2 GGUF at Q4_K_M (5.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.
- Which devices can run NVIDIA Nemotron Nano 9B v2 GGUF?
33 devices with unified memory can run NVIDIA Nemotron Nano 9B v2 GGUF at Q4_K_M (5.9 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.