Llama Guard 3 8B — Hardware Requirements & GPU Compatibility
Chat
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.
180.6K downloads
278 likesSpecifications
- Publisher
- Meta
- Family
- Llama
- Parameters
- 8.0B
- Release Date
- 2024-10-11
- 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 |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 2.4 GB | — | 2.21 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_XS | 2.40 | 2.6 GB | — | 2.41 GB | Importance-weighted 2-bit, extra small |
| IQ2_S | 2.50 | 2.8 GB | — | 2.51 GB | Importance-weighted 2-bit, small |
| IQ2_M | 2.70 | 3.0 GB | — | 2.71 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 3.4 GB | — | 3.11 GB | Importance-weighted 3-bit |
| Q2_K_S | 3.20 | 3.5 GB | — | 3.21 GB | 2-bit small K-quant |
| IQ3_XS | 3.30 | 3.6 GB | — | 3.31 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 3.8 GB | — | 3.41 GB | 2-bit quantization with K-quant improvements |
| IQ3_S | 3.40 | 3.8 GB | — | 3.41 GB | Importance-weighted 3-bit, small |
| Q3_K_S | 3.50 | 3.9 GB | — | 3.51 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 4.0 GB | — | 3.61 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 4.3 GB | — | 3.91 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.4 GB | — | 4.02 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 4.5 GB | — | 4.12 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 4.8 GB | — | 4.32 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 5.0 GB | — | 4.52 GB | 4-bit small quantization |
| IQ4_NL | 4.50 | 5.0 GB | — | 4.52 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 5.3 GB | — | 4.82 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 6.1 GB | — | 5.52 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 6.3 GB | — | 5.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.3 GB | — | 6.62 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.8 GB | — | 8.03 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama Guard 3 8B?
Q4_K_M · 5.3 GBLlama Guard 3 8B (Q4_K_M) requires 5.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Llama Guard 3 8B?
Q4_K_M · 5.3 GB33 devices with unified memory can run Llama Guard 3 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does Llama Guard 3 8B need?
Llama Guard 3 8B requires 5.3 GB of VRAM at Q4_K_M, or 8.8 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M5.3 GB- What's the best quantization for Llama Guard 3 8B?
For Llama Guard 3 8B, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.4 GB.
VRAM requirement by quantization
IQ2_XXS2.4 GB~53%Q2_K_S3.5 GB~71%Q3_K_M4.3 GB~83%IQ4_NL5.0 GB~88%Q4_K_M ★5.3 GB~89%Q8_08.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama Guard 3 8B on a Mac?
Llama Guard 3 8B requires at least 2.4 GB at IQ2_XXS, 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 Q4_K_M quantization it needs 5.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama Guard 3 8B?
At Q4_K_M, Llama Guard 3 8B can reach ~550 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~124 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 ÷ 5.3 × 0.55 = ~550 tok/s
Estimated speed at Q4_K_M (5.3 GB)
AMD Instinct MI300X~550 tok/sNVIDIA GeForce RTX 4090~124 tok/sNVIDIA H100 SXM~411 tok/sAMD Instinct MI250X~340 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 Q4_K_M, the download is about 4.82 GB. The full-precision Q8_0 version is 8.03 GB. The smallest option (IQ2_XXS) is 2.21 GB.