on

Can the AMD Ryzen Embedded V3C14 NAS (16 GB) run Qwen3Guard Gen 4B?

84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly

Very slow. At Q8_0 it needs about 6.5 GB of 12.0 GB usable and makes one decision in about 20.1 s.

Best quant
Q8_0
Time per decision
20.1 s
Decisions per second
0.0/s
Input assumed
512 tokens
Returns
Fixed labels

Best app to run it on the AMD Ryzen Embedded V3C14 NAS (16 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Usually the first to support new model architectures.
llama-server -hf mradermacher/Qwen3Guard-Gen-4B-GGUF:Q8_0
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…
  • LocalAI One OpenAI-compatible server for GGUF guard models and rerankers together.

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download Qwen3Guard Gen 4B

Recommended for the AMD Ryzen Embedded V3C14 NAS (16 GB): Q8_0. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q8_0 file (opens in a new tab)

huggingface.co/mradermacher/Qwen3Guard-Gen-4B-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/Qwen3Guard-Gen-4B-GGUF:Q8_0

Original weights · Hugging Face

Full-precision files from Alibaba, for fine-tuning, vLLM, Transformers.

huggingface.co/Qwen/Qwen3Guard-Gen-4B (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q8_0, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Very slow
How well it runs on the AMD Ryzen Embedded V3C14 NAS (16 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

PrecisionEditorial precision tier from the model’s published benchmarks, scaled by the quantization or precision used.37.8 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.25.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.1.1 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.12.0 / 15
Match score75.9 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the AMD Ryzen Embedded V3C14 NAS (16 GB)

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 4.6 GB 12.0 GB 19.5 s Very slow 79Great quality (A) · 79 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 5.0 GB 12.0 GB 19.6 s Very slow 81Great quality (A) · 81 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 5.5 GB 12.0 GB 19.8 s Very slow 83Great quality (A) · 83 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 6.5 GB 12.0 GB 20.1 s Very slow 84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 10.7 GB 12.0 GB 21.7 s Very slowtight fit 84Great quality (A) · 84 of 100: how capable the model is at F16, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab) Original weights (opens in a new tab)

Times are for one request with a 512-token input and about 6 tokens written. Batching servers process many requests at once.

Other hardware that runs it

Quick answers

How much memory does Qwen3Guard Gen 4B need on the AMD Ryzen Embedded V3C14 NAS 16GB?

At Q8_0 it needs about 6.5 GB. The AMD Ryzen Embedded V3C14 NAS 16GB has 12.0 GB usable.

How fast is Qwen3Guard Gen 4B on the AMD Ryzen Embedded V3C14 NAS 16GB?

It makes one decision in about 20.1 s at Q8_0, an estimate from the hardware and the size of the model.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

Was this page helpful?