Llama Guard 3 8B — Hardware Requirements & GPU Compatibility
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Meta Llama Guard 3 8B is an 8-billion parameter safety classifier model built on the Llama 3.1 architecture. Unlike general-purpose chat models, Llama Guard is specifically designed to classify whether prompts or responses contain unsafe content across categories such as violence, sexual content, criminal planning, and other policy violations. The model is intended to be used as a moderation layer in LLM-based applications, providing input and output safety filtering. It follows a taxonomy-based classification approach and can be customized for different safety policies. Released under the Llama 3.1 Community License.
46.1K downloads
327 likesSpecifications
- Publisher
- Meta
- Family
- Llama
- Parameters
- 8.0B
- Release Date
- 2024-07-22
- License
- Llama 3.1 Community
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HuggingFace
How Much VRAM Does Llama Guard 3 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 17.7 GB | — | 16.06 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Llama Guard 3 8B?
BF16 · 17.7 GBLlama Guard 3 8B (BF16) requires 17.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Llama Guard 3 8B?
BF16 · 17.7 GB41 devices with unified memory can run Llama Guard 3 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Llama Guard 3 8B need?
Llama Guard 3 8B requires 17.7 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 16 bits ÷ 8 = 16.1 GB
KV Cache + Overhead ≈ 1.6 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1617.7 GB- Can I run Llama Guard 3 8B on a Mac?
Llama Guard 3 8B requires at least 17.7 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 Llama Guard 3 8B locally?
Yes — Llama Guard 3 8B can run locally on consumer hardware. At BF16 quantization it needs 17.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama Guard 3 8B?
At BF16, Llama Guard 3 8B can reach ~272 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~37 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 ÷ 17.7 × 0.65 = ~294 tok/s
Estimated speed at BF16 (17.7 GB)
~294 tok/s~37 tok/s~294 tok/s~272 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama Guard 3 8B?
At BF16, the download is about 16.06 GB.
- Which GPUs can run Llama Guard 3 8B?
8 consumer GPUs can run Llama Guard 3 8B at BF16 (17.7 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Llama Guard 3 8B?
41 devices with unified memory can run Llama Guard 3 8B at BF16 (17.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.