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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~137 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~29 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Wildguard
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve allenai/wildguard --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~124 tok/s
- generation speed
- Cost per 1M output tokens
- $0.27
- per 1M output tokens
How Much VRAM Does Wildguard Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.4 GB | 8.7 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~193 tok/s | |
| 3.9 GB | 9.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~168 tok/s | |
| 4.8 GB | 10.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~137 tok/s | |
| 5.7 GB | 11 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~115 tok/s | |
| 6.6 GB | 11.9 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~100 tok/s | |
| 8.0 GB | 13.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~82 tok/s | |
| 15.9 GB | 21.3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~41 tok/s |
Selected Q4_K_M
4-bit medium quantization7.3GBat 16K context
- Weights
- 4.1 GB
- KV cache
- 2.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.7 GB free · ~137 tok/s
≥50 tok/s needs ≥367 GB/s · RTX 5090 ~244, RTX 3090 Ti ~137
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run Wildguard, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 0.7 GB free · up to 16K ctx
16 GPUs · 2 device families
RTX 3080~103Simulate output on RTX 3080RTX 2080 Ti~84Simulate output on RTX 2080 TiRTX 3070 Ti~83Simulate output on RTX 3070 TiOrin Nano~14Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~10Simulate output on iPhone 1720 GB
fits · 12.7 GB free · up to 32K ctx
1 GPU
RX 7900 XT~100Simulate output on RX 7900 XT32–40 GB
fits · 24.7 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~244Simulate output on RTX 5090M5 Max36 GB~67Simulate output on Mac Studio M5 Max (36 GB)M532 GB~23Simulate output on MacBook Air 13" M5 (32 GB)Orin~28Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~18Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen2.5 7B InstructAlibaba | 65.1 | 5.8 GB | ~131 | same size |
| Phi Mini MoE InstructMicrosoft | — | 5.4 GB | ~239 | same size |
| Falcon H1 7B InstructTII UAE | — | 6.3 GB | ~130 | same size |
| DeepSeek R1 Distill Qwen 7BDeepSeek | — | 5.8 GB | ~131 | same size |
| Mistral 7B Instruct v0.3Mistral AI | — | 6.8 GB | ~133 | same size |
| Phi 3 Small 8k InstructMicrosoft | — | 16.2 GB | ~43 | same size |
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Wildguard on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Wildguard. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 4.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Wildguard locally?
Yes — Wildguard can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Wildguard?
At Q4_K_M, Wildguard can reach ~1004 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 4.78 × 0.65 = ~1088 tok/s
Estimated speed at Q4_K_M (4.8 GB)
Real-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?
52 consumer GPUs can run Wildguard at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Wildguard?
151 devices with unified memory can run Wildguard at Q4_K_M (4.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.