on

Can the NVIDIA RTX A500 Laptop GPU run DynaGuard 8B?

79Great quality (A) · 79 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly (heavy RAM offload)

Very slow (heavy RAM offload). At Q8_0 it needs about 9.8 GB of 4.0 GB usable and makes one decision in about 28.8 s.

Best quant
Q8_0
Time per decision
28.8 s
Decisions per second
0.0/s
Input assumed
512 tokens
Returns
Reasoned verdict

Best app to run it on the NVIDIA RTX A500 Laptop GPU

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Point-and-click: search, download and chat in one window.

Open DynaGuard 8B in LM Studio

  • llama.cpp Splits the model between GPU memory and system RAM, which this setup needs.
  • Ollama Splits the model between GPU memory and system RAM, which this setup needs.

On Linux

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Point-and-click: search, download and chat in one window.

Open DynaGuard 8B in LM Studio

  • llama.cpp Splits the model between GPU memory and system RAM, which this setup needs.
  • Ollama Splits the model between GPU memory and system RAM, which this setup needs.

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 8B

Recommended for the NVIDIA RTX A500 Laptop GPU: 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/DynaGuard-8B-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/DynaGuard-8B-GGUF:Q8_0

Original weights · Hugging Face

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

huggingface.co/tomg-group-umd/DynaGuard-8B (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 79Great quality (A) · 79 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 50/100 (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 slowheavy RAM offload
How well it runs on the NVIDIA RTX A500 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.35.6 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.4.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.0.9 / 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 score50.4 / 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 A500 Laptop GPU

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 6.1 GB 4.0 GB 13.4 s Very slowheavy RAM offload 75Great quality (A) · 75 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 49/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 6.9 GB 4.0 GB 16.8 s Very slowheavy RAM offload 76Great quality (A) · 76 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 49/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 7.8 GB 4.0 GB 20.4 s Very slowheavy RAM offload 78Great quality (A) · 78 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 9.8 GB 4.0 GB 28.8 s Very slowheavy RAM offload 79Great quality (A) · 79 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 17.5 GB 4.0 GB — Won't run 79Great quality (A) · 79 of 100: how capable the model is at F16, whatever it runs on. Match score 10/100 (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 8B need on the NVIDIA RTX A500 Laptop GPU 4GB?

At Q8_0 it needs about 9.8 GB. The NVIDIA RTX A500 Laptop GPU 4GB has 4.0 GB usable.

How fast is DynaGuard 8B on the NVIDIA RTX A500 Laptop GPU 4GB?

It makes one decision in about 28.8 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.

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