Wildguard — Hardware Requirements & GPU Compatibility
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Wildguard is a 7.2B-parameter open language model from Allen AI. At Q4_K_M it needs about 4.78 GB of VRAM — see which GPUs and Macs can run it below.
154.1K downloads
49 likesSpecifications
- Publisher
- Allen AI
- Parameters
- 7.2B
- Release Date
- 2024-06-15
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Wildguard Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 3.4 GB | — | 3.08 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 3.9 GB | — | 3.53 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 4.8 GB | — | 4.35 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 5.7 GB | — | 5.16 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 6.6 GB | — | 5.98 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 8.0 GB | — | 7.25 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 15.9 GB | — | 14.50 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 Wildguard?
Q4_K_M · 4.8 GBWildguard (Q4_K_M) requires 4.8 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 Wildguard?
Q4_K_M · 4.8 GB33 devices with unified memory can run Wildguard, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Wildguard need?
Wildguard requires 4.8 GB of VRAM at Q4_K_M, or 15.9 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 7.2B × 4.8 bits ÷ 8 = 4.3 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M4.8 GB- What's the best quantization for Wildguard?
For Wildguard, Q4_K_M (4.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (5.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.4 GB.
VRAM requirement by quantization
Q2_K3.4 GBQ4_K_M ★4.8 GBQ5_K_M5.7 GBQ6_K6.6 GBQ8_08.0 GBBF1615.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Wildguard on a Mac?
Wildguard requires at least 3.4 GB at Q2_K, 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 Wildguard locally?
Yes — Wildguard can run locally on consumer hardware. At Q4_K_M quantization it needs 4.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Wildguard?
At Q4_K_M, Wildguard can reach ~610 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~137 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 ÷ 4.8 × 0.55 = ~610 tok/s
Estimated speed at Q4_K_M (4.8 GB)
~610 tok/s~137 tok/s~456 tok/s~377 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Wildguard?
At Q4_K_M, the download is about 4.35 GB. The full-precision BF16 version is 14.50 GB. The smallest option (Q2_K) is 3.08 GB.
- Which GPUs can run Wildguard?
35 consumer GPUs can run Wildguard at Q4_K_M (4.8 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 Wildguard?
33 devices with unified memory can run Wildguard at Q4_K_M (4.8 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.