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Can the NVIDIA RTX 500 Ada Generation Laptop GPU run DynaGuard 1.7B?

69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly

Very slow. At Q6_K it needs about 2.7 GB of 4.0 GB usable and makes one decision in about 3.1 s.

Best quant
Q6_K
Time per decision
3.1 s
Decisions per second
0.3/s
Input assumed
512 tokens
Returns
Reasoned verdict

Best app to run it on the NVIDIA RTX 500 Ada Generation Laptop GPU

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Point-and-click: search, download and chat in one window.

Open DynaGuard 1.7B in LM Studio

  • llama.cpp Usually the first to support new model architectures.
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

On Linux

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Point-and-click: search, download and chat in one window.

Open DynaGuard 1.7B in LM Studio

  • llama.cpp Usually the first to support new model architectures.
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

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 DynaGuard 1.7B

Recommended for the NVIDIA RTX 500 Ada Generation Laptop GPU: 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/DynaGuard-1.7B-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/DynaGuard-1.7B-GGUF:Q6_K

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 69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 68/100 (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 Very slow
How well it runs on the NVIDIA RTX 500 Ada Generation Laptop GPU: 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.31.2 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.25.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.2.7 / 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 score68.9 / 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 NVIDIA RTX 500 Ada Generation Laptop GPU

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 2.3 GB 4.0 GB 2.2 s Very slow 66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 2.5 GB 4.0 GB 2.7 s Very slow 67Good quality (B) · 67 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 2.7 GB 4.0 GB 3.1 s Very slow 69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 3.2 GB 4.0 GB 4.2 s Very slow 70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 5.1 GB 4.0 GB 11.2 s Very slowoffloads to RAM 70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 52/100 (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 NVIDIA RTX 500 Ada Generation Laptop GPU 4GB?

At Q6_K it needs about 2.7 GB. The NVIDIA RTX 500 Ada Generation Laptop GPU 4GB has 4.0 GB usable.

How fast is DynaGuard 1.7B on the NVIDIA RTX 500 Ada Generation Laptop GPU 4GB?

It makes one decision in about 3.1 s 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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