DynaGuard 8B on NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition

Can the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition run DynaGuard 8B?

Yes, but slowly

Slow. At Q6_K it needs about 12.4 GB of 96.0 GB usable and makes one decision in about 916 ms.

Best quant: Q6_K
Time per decision: 916 ms
Decisions per second: 1.1/s
Input assumed: 512 tokens
Returns: Reasoned verdict

Best app to run it on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

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

Open DynaGuard 8B 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

vLLM

Inference server · runs on vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

vllm serve tomg-group-umd/DynaGuard-8B

LM Studio Point-and-click: search, download and chat in one window.
llama.cpp Usually the first to support new model architectures.

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 PRO 6000 Blackwell Max-Q Workstation Edition: 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-8B-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/DynaGuard-8B-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-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 78Great quality (A) · 78 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.: 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 Slow: How well it runs on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition: 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.	35.2 / 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.	5.0 / 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 score	75.2 / 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 PRO 6000 Blackwell Max-Q Workstation Edition

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q4_K_M`	10.7 GB	96.0 GB	653 ms	Slow	75Great quality (A) · 75 of 100: how capable the model is at Q4_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)
`Q5_K_M`	11.5 GB	96.0 GB	778 ms	Slow	76Great quality (A) · 76 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`	12.4 GB	96.0 GB	916 ms	Slow	78Great quality (A) · 78 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`	14.4 GB	96.0 GB	1.2 s	Slow	79Great quality (A) · 79 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	22.1 GB	96.0 GB	2.6 s	Very slow	79Great quality (A) · 79 of 100: how capable the model is at F16, whatever it runs on. Match score 73/100 (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

75Great quality (A) · 75 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA B200 Q4_K_M · 174 ms · Runs well
78Great quality (A) · 78 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI350X Q6_K · 277 ms · Runs well
78Great quality (A) · 78 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip Q6_K · 293 ms · Runs well
78Great quality (A) · 78 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI325X Q6_K · 343 ms · Runs well
78Great quality (A) · 78 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H200 141GB SXM Q6_K · 368 ms · Runs well
78Great quality (A) · 78 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H200 NVL Q6_K · 373 ms · Runs well

Quick answers

How much memory does DynaGuard 8B need on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB?

At Q6_K it needs about 12.4 GB. The NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB has 96.0 GB usable.

How fast is DynaGuard 8B on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB?

It makes one decision in about 916 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.