Llama Guard 3 8B IMat GGUF — Hardware Requirements & GPU Compatibility
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779 downloads
5 likesSpecifications
- Publisher
- legraphista
- Family
- Llama
- Parameters
- 8B
- License
- Llama 3.1 Community
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HuggingFace
How Much VRAM Does Llama Guard 3 8B IMat 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 |
| Q4_K_S | 4.50 | 5.0 GB | — | 4.50 GB | 4-bit small quantization |
| 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 IMat GGUF?
Q4_K_S · 5.0 GBLlama Guard 3 8B IMat GGUF (Q4_K_S) requires 5.0 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 IMat GGUF?
Q4_K_S · 5.0 GB33 devices with unified memory can run Llama Guard 3 8B IMat 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 IMat GGUF need?
Llama Guard 3 8B IMat GGUF requires 2.4 GB of VRAM at IQ2_XXS, or 8.8 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 2.2 bits ÷ 8 = 2.2 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
IQ2_XXS2.4 GB- What's the best quantization for Llama Guard 3 8B IMat GGUF?
For Llama Guard 3 8B IMat GGUF, Q3_K_S (3.9 GB) offers the best balance of quality and VRAM usage. IQ3_M (4.0 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%IQ3_XXS3.4 GB~70%Q3_K_S ★3.9 GB~77%IQ3_M4.0 GB~78%IQ4_NL5.0 GB~88%Q8_08.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama Guard 3 8B IMat GGUF on a Mac?
Llama Guard 3 8B IMat GGUF 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 IMat GGUF locally?
Yes — Llama Guard 3 8B IMat GGUF can run locally on consumer hardware. At IQ2_XXS quantization it needs 2.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama Guard 3 8B IMat GGUF?
At IQ2_XXS, Llama Guard 3 8B IMat GGUF can reach ~1205 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~271 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 ÷ 2.4 × 0.55 = ~1205 tok/s
Estimated speed at IQ2_XXS (2.4 GB)
AMD Instinct MI300X~1205 tok/sNVIDIA GeForce RTX 4090~271 tok/sNVIDIA H100 SXM~900 tok/sAMD Instinct MI250X~745 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 IMat GGUF?
At IQ2_XXS, the download is about 2.20 GB. The full-precision Q8_0 version is 8.00 GB.
- Which GPUs can run Llama Guard 3 8B IMat GGUF?
35 consumer GPUs can run Llama Guard 3 8B IMat GGUF at IQ2_XXS (2.4 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 IMat GGUF?
33 devices with unified memory can run Llama Guard 3 8B IMat GGUF at IQ2_XXS (2.4 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.