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Can the Apple M1 Pro (16 GB) run Qwen3Guard Gen 0.6B?

73Great quality (A) · 73 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q6_K it needs about 2.3 GB of 10.7 GB usable and makes one decision in about 154 ms.

Best quant
Q6_K
Time per decision
154 ms
Decisions per second
6.5/s
Input assumed
512 tokens
Returns
Fixed labels

Best app to run it on the Apple M1 Pro (16 GB)

On macOS

LM Studio

Desktop app · runs on llama.cpp, MLX · use the MLX build

  • Runs the MLX build, typically 35–90% faster than GGUF for a model with 0.8B active parameters on Apple silicon.
  • Point-and-click: search, download and chat in one window.

Open Qwen3Guard Gen 0.6B in LM Studio

  • MLX LM Runs the MLX build, typically 35–90% faster than GGUF for a model with 0.8B active parameters on Apple silicon.
  • Unsloth Desktop Runs the MLX build, typically 35–90% faster than GGUF for a model with 0.8B active parameters on Apple silicon.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Qwen3Guard Gen 0.6B

Recommended for the Apple M1 Pro (16 GB): Q6_K. 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 Q6_K file (opens in a new tab)

huggingface.co/mradermacher/Qwen3Guard-Gen-0.6B-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/Qwen3Guard-Gen-0.6B-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3Guard-Gen-0.6B (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 73Great quality (A) · 73 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q6_K, 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 Runs well
How well it runs on the Apple M1 Pro (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.33.0 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; 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.12.1 / 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 score82.1 / 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 Apple M1 Pro (16 GB)

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 2.1 GB 10.7 GB 144 ms Runs well 70Great quality (A) · 70 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 2.2 GB 10.7 GB 149 ms Runs well 71Great quality (A) · 71 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 2.3 GB 10.7 GB 154 ms Runs well 73Great quality (A) · 73 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 2.5 GB 10.7 GB 165 ms Runs well 74Great quality (A) · 74 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 3.2 GB 10.7 GB 214 ms Runs well 74Great quality (A) · 74 of 100: how capable the model is at F16, whatever it runs on. Match score 80/100 (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 0.6B need on the M1 Pro 16GB?

At Q6_K it needs about 2.3 GB. The M1 Pro 16GB has 10.7 GB usable.

How fast is Qwen3Guard Gen 0.6B on the M1 Pro 16GB?

It makes one decision in about 154 ms at Q6_K, 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.

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