Meta Llama Guard 2 8B — Hardware Requirements & GPU Compatibility
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Meta Llama Guard 2 8B is a 8.0B-parameter open language model from Meta in the Llama family. At BF16 it needs about 17.67 GB of VRAM — see which GPUs and Macs can run it below.
8.3K downloads
307 likesSpecifications
- Publisher
- Meta
- Family
- Llama
- Parameters
- 8.0B
- Release Date
- 2024-04-17
- License
- Llama 3 Community
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HuggingFace
How Much VRAM Does Meta Llama Guard 2 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 Meta Llama Guard 2 8B?
BF16 · 17.7 GBMeta Llama Guard 2 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 Meta Llama Guard 2 8B?
BF16 · 17.7 GB41 devices with unified memory can run Meta Llama Guard 2 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 Meta Llama Guard 2 8B need?
Meta Llama Guard 2 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 Meta Llama Guard 2 8B on a Mac?
Meta Llama Guard 2 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 Meta Llama Guard 2 8B locally?
Yes — Meta Llama Guard 2 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 Meta Llama Guard 2 8B?
At BF16, Meta Llama Guard 2 8B can reach ~249 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~249 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Meta Llama Guard 2 8B?
At BF16, the download is about 16.06 GB.
- Which GPUs can run Meta Llama Guard 2 8B?
8 consumer GPUs can run Meta Llama Guard 2 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 Meta Llama Guard 2 8B?
41 devices with unified memory can run Meta Llama Guard 2 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.