Llama Guard 3 8B GGUF — Hardware Requirements & GPU Compatibility
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262 downloads
1 likesSpecifications
- Publisher
- mradermacher
- Family
- Llama
- Parameters
- 8B
- License
- Llama 3.1 Community
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HuggingFace
How Much VRAM Does Llama Guard 3 8B GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.7 GB | — | 3.40 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.9 GB | — | 3.50 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.3 GB | — | 3.90 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 5.3 GB | — | 4.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.3 GB | — | 5.70 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.3 GB | — | 6.60 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.8 GB | — | 8.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama Guard 3 8B GGUF?
Q4_K_M · 5.3 GBLlama Guard 3 8B GGUF (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 GGUF?
Q4_K_M · 5.3 GB33 devices with unified memory can run Llama Guard 3 8B GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Llama Guard 3 8B GGUF need?
Llama Guard 3 8B GGUF requires 5.3 GB of VRAM at Q4_K_M, or 8.8 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 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 GGUF?
For Llama Guard 3 8B GGUF, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.0 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 3.6 GB.
VRAM requirement by quantization
IQ3_XS3.6 GB~73%Q3_K_S3.9 GB~77%IQ4_XS4.7 GB~87%Q4_K_M ★5.3 GB~89%Q5_K_S6.0 GB~92%Q8_08.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama Guard 3 8B GGUF on a Mac?
Llama Guard 3 8B GGUF requires at least 3.6 GB at IQ3_XS, 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 GGUF locally?
Yes — Llama Guard 3 8B GGUF 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 GGUF?
At Q4_K_M, Llama Guard 3 8B GGUF can reach ~552 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 = ~552 tok/s
Estimated speed at Q4_K_M (5.3 GB)
AMD Instinct MI300X~552 tok/sNVIDIA GeForce RTX 4090~124 tok/sNVIDIA H100 SXM~413 tok/sAMD Instinct MI250X~341 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 GGUF?
At Q4_K_M, the download is about 4.80 GB. The full-precision Q8_0 version is 8.00 GB. The smallest option (IQ3_XS) is 3.30 GB.
- Which GPUs can run Llama Guard 3 8B GGUF?
35 consumer GPUs can run Llama Guard 3 8B GGUF at Q4_K_M (5.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llama Guard 3 8B GGUF?
33 devices with unified memory can run Llama Guard 3 8B GGUF at Q4_K_M (5.3 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.