Nemotron Mini 4B Instruct — Hardware Requirements & GPU Compatibility
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Nemotron Mini 4B Instruct is a 4B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 2.97 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~221 tok/s · 4K)Macs from 8 GB (Q4_K_M · ~24 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Nemotron Mini 4B Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run nemotron-mini:4bQ4_K_M fits fully on the RTX 4090 · 20.8 GB free · 4K context
Download: Nemotron-Mini-4B-Instruct-Q4_K_M.gguf2.70 GBbartowski
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (8 GB headroom) · Q4_K_M
- Generation speed
- ~79 tok/s
- generation speed
- Cost per 1M output tokens
- $0.29
- per 1M output tokens
How Much VRAM Does Nemotron Mini 4B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~289 tok/s | |
| 2.3 GB | — | 3-bit small — 3-bit small quantization | Fits: 4K context · ~282 tok/s | |
| 2.5 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~260 tok/s | |
| 2.6 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 4K context · ~255 tok/s | |
| 3.0 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~221 tok/s | |
| 3.4 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~192 tok/s | |
| 3.9 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~169 tok/s | |
| 4.6 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~143 tok/s |
Selected Q4_K_M
4-bit medium quantization3.2GBat 4K context
- Weights
- 2.4 GB
- KV cache
- 0.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.8 GB free · ~221 tok/s
≥50 tok/s needs ≥228 GB/s · RTX 5090 ~392, RTX 3090 Ti ~221
Which Hardware Can Run Nemotron Mini 4B Instruct?
Q4_K_M · 3.0 GBNemotron Mini 4B Instruct (Q4_K_M) requires 3.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 4K context window can add up to 0.3 GB, bringing total usage to 3.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Nemotron Mini 4B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.8 GB free · up to 4K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~166Simulate output on RTX 3080RTX 2080 Ti~135Simulate output on RTX 2080 TiRTX 3070 Ti~133Simulate output on RTX 3070 TiM3*8 GB~24Simulate output on iMac 24" M3 (8 GB)Orin Nano~22Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~16Simulate output on iPhone 1720 GB
fits · 16.8 GB free · up to 4K ctx
1 GPU
RX 7900 XT~162Simulate output on RX 7900 XT32–40 GB
fits · 28.8 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~392Simulate output on RTX 5090M5 Max36 GB~108Simulate output on Mac Studio M5 Max (36 GB)M532 GB~36Simulate output on MacBook Air 13" M5 (32 GB)Orin~45Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~30Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Nemotron Mini 4B Instruct
Nemotron family · 42 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Gemma 3 4B ITGoogle | 64.0 | 5 GB | ~231 | same size |
| Granite Vision 4.1 4BIBM | — | 4 GB | ~228 | same size |
| Qwen3 4B Instruct 2507Alibaba | — | 4.2 GB | ~226 | same size |
| MiniCPM V 4OpenBMB | — | 3.1 GB | ~236 | same size |
| Phi 4 Mini ReasoningMicrosoft | — | 4.8 GB | ~228 | same size |
| Qwen2.5 VL 3B InstructAlibaba | — | 3.2 GB | ~249 | same size |
Specifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 4B
- Architecture
- NemotronForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 256,000
- Release Date
- 2024-09-10
- License
- Other
Frequently Asked Questions
How much VRAM does Nemotron Mini 4B Instruct need?
Nemotron Mini 4B Instruct requires 3.0 GB of VRAM at Q4_K_M, or 8.6 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 4B × 4.8 bits ÷ 8 = 2.4 GB
KV Cache + Overhead ≈ 0.6 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 ≈ 0.8 GB (at full 4K context)
VRAM usage by quantization
What's the best quantization for Nemotron Mini 4B Instruct?
For Nemotron Mini 4B Instruct, Q4_K_M (3.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (3.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 1.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Nemotron Mini 4B Instruct on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Nemotron Mini 4B Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 3.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run Nemotron Mini 4B Instruct locally?
Yes — Nemotron Mini 4B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Nemotron Mini 4B Instruct?
At Q4_K_M, Nemotron Mini 4B Instruct can reach ~1616 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~221 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 ÷ 2.97 × 0.65 = ~1751 tok/s
Estimated speed at Q4_K_M (3.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Nemotron Mini 4B Instruct?
At Q4_K_M, the download is about 2.40 GB. The full-precision BF16 version is 8.00 GB. The smallest option (IQ2_XS) is 1.20 GB.
Which GPUs can run Nemotron Mini 4B Instruct?
52 consumer GPUs can run Nemotron Mini 4B Instruct at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Nemotron Mini 4B Instruct?
163 devices with unified memory can run Nemotron Mini 4B Instruct at Q4_K_M (3.0 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.