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Can the Annapurna Labs Alpine AL524 NAS (4 GB) run DynaGuard 1.7B?

67Good quality (B) · 67 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly (far over memory limit)

Very slow (far over memory limit). At Q5_K_M it needs about 2.4 GB of 2.0 GB usable and makes one decision in about 167.9 s.

Best quant
Q5_K_M
Time per decision
167.9 s
Decisions per second
0.0/s
Input assumed
512 tokens
Returns
Reasoned verdict

Best app to run it on the Annapurna Labs Alpine AL524 NAS (4 GB)

On Linux on ARM boards

llama.cpp

Command line + local API · runs on llama.cpp

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Gives you direct control of how many layers sit on the GPU, the best way to squeeze a model that doesn’t quite fit.
  • Usually the first to support new model architectures.
llama-server -hf mradermacher/DynaGuard-1.7B-GGUF:Q5_K_M
  • Ollama Splits the model between GPU memory and system RAM, which this setup needs.
  • 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 DynaGuard 1.7B

Recommended for the Annapurna Labs Alpine AL524 NAS (4 GB): Q5_K_M. 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 Q5_K_M file (opens in a new tab)

huggingface.co/mradermacher/DynaGuard-1.7B-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/DynaGuard-1.7B-GGUF:Q5_K_M

Original weights · Hugging Face

Full-precision files from University of Maryland, for fine-tuning, vLLM, Transformers.

huggingface.co/tomg-group-umd/DynaGuard-1.7B (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 67Good quality (B) · 67 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q5_K_M, 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 slowfar over memory limit
How well it runs on the Annapurna Labs Alpine AL524 NAS (4 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.30.6 / 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.4.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.0.4 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.10.0 / 15
Match score44.9 / 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 Annapurna Labs Alpine AL524 NAS (4 GB)

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 2.2 GB 2.0 GB 153.2 s Very slowfar over memory limit 66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 2.4 GB 2.0 GB 167.9 s Very slowfar over memory limit 67Good quality (B) · 67 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 2.7 GB 2.0 GB — Won't run 69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 3.1 GB 2.0 GB — Won't run 70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 5.1 GB 2.0 GB — Won't run 70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 9/100 (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 160 tokens written. Batching servers process many requests at once.

Other hardware that runs it

Quick answers

How much memory does DynaGuard 1.7B need on the Annapurna Labs Alpine AL524 NAS 4GB?

At Q5_K_M it needs about 2.4 GB. The Annapurna Labs Alpine AL524 NAS 4GB has 2.0 GB usable.

How fast is DynaGuard 1.7B on the Annapurna Labs Alpine AL524 NAS 4GB?

It makes one decision in about 167.9 s at Q5_K_M, 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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