Qwen3Guard Gen 4B — Hardware Requirements & GPU Compatibility
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Qwen3Guard Gen 4B is a 4.4B-parameter open language model from Alibaba in the Qwen 3 family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 3.14 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 · 32K)Best on 24 GB (Q4_K_M · ~209 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~23 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3Guard Gen 4B
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
ollama run hf.co/mradermacher/Qwen3Guard-Gen-4B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 19.5 GB free · 16K context
Download: Qwen3Guard-Gen-4B.Q4_K_M.gguf2.72 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (7 GB headroom) · Q4_K_M
- Generation speed
- ~75 tok/s
- generation speed
- Cost per 1M output tokens
- $0.22
- per 1M output tokens
How Much VRAM Does Qwen3Guard Gen 4B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.4 GB | 5.2 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~278 tok/s | |
| 2.4 GB | 5.3 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~271 tok/s | |
| 2.6 GB | 5.5 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~248 tok/s | |
| 2.8 GB | 5.6 GB | 3-bit large — 3-bit large quantization | Fits: 32K context · ~238 tok/s | |
| 3.1 GB | 6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~209 tok/s | |
| 3.6 GB | 6.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~181 tok/s | |
| 4.1 GB | 7 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~159 tok/s | |
| 4.9 GB | 7.7 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~134 tok/s |
Selected Q4_K_M
4-bit medium quantization4.5GBat 16K context
- Weights
- 2.7 GB
- KV cache
- 1.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 19.5 GB free · ~209 tok/s
≥50 tok/s needs ≥241 GB/s · RTX 5090 ~371, RTX 3090 Ti ~209
Which Hardware Can Run Qwen3Guard Gen 4B?
Q4_K_M · 3.1 GBQwen3Guard Gen 4B (Q4_K_M) requires 3.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 33K context window can add up to 2.8 GB, bringing total usage to 6.0 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Qwen3Guard Gen 4B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 3.5 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~157Simulate output on RTX 3080RTX 2080 Ti~128Simulate output on RTX 2080 TiRTX 3070 Ti~126Simulate output on RTX 3070 TiM3*8 GB~23Simulate output on iMac 24" M3 (8 GB)Orin Nano~21Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~15Simulate output on iPhone 1720 GB
fits · 15.5 GB free · up to 32K ctx
1 GPU
RX 7900 XT~153Simulate output on RX 7900 XT32–40 GB
fits · 27.5 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~371Simulate output on RTX 5090M5 Max36 GB~103Simulate output on Mac Studio M5 Max (36 GB)M532 GB~34Simulate output on MacBook Air 13" M5 (32 GB)Orin~42Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~28Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3Guard Gen 4B
Qwen 3 family · 79 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Spark X2.6 4BXHToken | — | 4.3 GB | ~221 | same size |
| LightOnOCR 3 4Blightonai | — | 3.4 GB | ~205 | same size |
| FrogNano 4B 2609Microsoft | — | 3.4 GB | ~201 | same size |
| Qianfan OCRBaidu | — | 4.7 GB | ~197 | same size |
| Translategemma 4B ITGoogle | — | 5 GB | ~231 | same size |
| Medgemma 1.5 4B ITGoogle | — | 5 GB | ~231 | same size |
Specifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 4.4B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-09-23
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen3Guard Gen 4B need?
Qwen3Guard Gen 4B requires 3.1 GB of VRAM at Q4_K_M, or 9.3 GB at BF16. Full 33K context adds up to 2.8 GB (6.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.4B × 4.8 bits ÷ 8 = 2.6 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.
KV Cache + Overhead ≈ 3.4 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for Qwen3Guard Gen 4B?
For Qwen3Guard Gen 4B, Q4_K_M (3.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (3.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3Guard Gen 4B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen3Guard Gen 4B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 3.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3Guard Gen 4B locally?
Yes — Qwen3Guard Gen 4B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3Guard Gen 4B?
At Q4_K_M, Qwen3Guard Gen 4B can reach ~1529 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~209 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 ÷ 3.14 × 0.65 = ~1656 tok/s
Estimated speed at Q4_K_M (3.1 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen3Guard Gen 4B?
At Q4_K_M, the download is about 2.65 GB. The full-precision BF16 version is 8.82 GB. The smallest option (Q2_K) is 1.87 GB.
Which GPUs can run Qwen3Guard Gen 4B?
52 consumer GPUs can run Qwen3Guard Gen 4B 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 Qwen3Guard Gen 4B?
163 devices with unified memory can run Qwen3Guard Gen 4B at Q4_K_M (3.1 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.