Nemotron Content Safety Reasoning 4B — Hardware Requirements & GPU Compatibility
ChatReasoning
Nemotron Content Safety Reasoning 4B is a 4.3B-parameter open language model from NVIDIA. It supports a context window of up to 131,072 tokens. At BF16 it needs about 9.26 GB of VRAM — see which GPUs and Macs can run it below.
2.3K downloads
19 likesSpecifications
- Publisher
- NVIDIA
- Parameters
- 4.3B
- Architecture
- Gemma3ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,208
- Release Date
- 2025-12-06
- License
- Other
Get Started
HuggingFace
How Much VRAM Does Nemotron Content Safety Reasoning 4B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 9.3 GB | 31.7 GB | 8.60 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Nemotron Content Safety Reasoning 4B?
BF16 · 9.3 GBNemotron Content Safety Reasoning 4B (BF16) requires 9.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. Using the full 131K context window can add up to 22.5 GB, bringing total usage to 31.7 GB. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Nemotron Content Safety Reasoning 4B?
BF16 · 9.3 GB27 devices with unified memory can run Nemotron Content Safety Reasoning 4B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Nemotron Content Safety Reasoning 4B need?
Nemotron Content Safety Reasoning 4B requires 9.3 GB of VRAM at BF16. Full 131K context adds up to 22.5 GB (31.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.3B × 16 bits ÷ 8 = 8.6 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 23.1 GB (at full 131K context)
VRAM usage by quantization
BF169.3 GBBF16 + full context31.7 GB- Can I run Nemotron Content Safety Reasoning 4B on a Mac?
Nemotron Content Safety Reasoning 4B requires at least 9.3 GB at BF16, 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 Nemotron Content Safety Reasoning 4B locally?
Yes — Nemotron Content Safety Reasoning 4B can run locally on consumer hardware. At BF16 quantization it needs 9.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Nemotron Content Safety Reasoning 4B?
At BF16, Nemotron Content Safety Reasoning 4B can reach ~315 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~71 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 ÷ 9.3 × 0.55 = ~315 tok/s
Estimated speed at BF16 (9.3 GB)
~315 tok/s~71 tok/s~235 tok/s~195 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Nemotron Content Safety Reasoning 4B?
At BF16, the download is about 8.60 GB.
- Which GPUs can run Nemotron Content Safety Reasoning 4B?
28 consumer GPUs can run Nemotron Content Safety Reasoning 4B at BF16 (9.3 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Nemotron Content Safety Reasoning 4B?
27 devices with unified memory can run Nemotron Content Safety Reasoning 4B at BF16 (9.3 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.