GLiGuard 300M on LattePanda 3 Delta (8 GB)

Can the LattePanda 3 Delta (8 GB) run GLiGuard 300M?

Yes, it runs well

Runs well. At Q8_0 it needs about 0.9 GB of 4.0 GB usable and makes one decision in about 352 ms.

Best precision: Q8_0
Time per decision: 352 ms
Decisions per second: 2.8/s
Input assumed: 512 tokens
Returns: Probability or score

Best app to run it on the LattePanda 3 Delta (8 GB)

On Windows

GLiNER

Python library · runs on PyTorch, ONNX

Can only use the CPU on this hardware, so it will be slow.
Made for this model family: every label is scored in one pass, fast even on a CPU.

pip install gliner2

On Linux

GLiNER

Python library · runs on PyTorch, ONNX

Can only use the CPU on this hardware, so it will be slow.
Made for this model family: every label is scored in one pass, fast even on a CPU.

pip install gliner2

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 GLiGuard 300M

Original weights · Hugging Face

Full-precision files from Fastino, for Transformers, Sentence Transformers, Text Embeddings Inference, Infinity or vLLM.

huggingface.co/fastino/gliguard-LLMGuardrails-300M (opens in a new tab)

No sign-in needed.

Links checked 3 hours ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 68Good quality (B) · 68 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 78/100 (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 Runs well: How well it runs on the LattePanda 3 Delta (8 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 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.	8.0 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.	15.0 / 15
Match score	78.6 / 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 LattePanda 3 Delta (8 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q8_0`	0.9 GB	4.0 GB	352 ms	Runs well	68Good quality (B) · 68 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`F16`	1.1 GB	4.0 GB	405 ms	Slow	68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Times are for one request with a 512-token input. Batching servers process many requests at once.

Other hardware that runs it

68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX F16 · 5.8 ms · Runs great
68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT F16 · 6.2 ms · Runs great
68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. AMD Ryzen AI Max+ 395 (128 GB) F16 · 7.6 ms · Runs great
68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 (16 GB) F16 · 87 ms · Runs great
68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 Max (64 GB) F16 · 25 ms · Runs great
68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. Apple M2 Ultra (192 GB) F16 · 12 ms · Runs great

Quick answers

How much memory does GLiGuard 300M need on the LattePanda 3 Delta 8GB?

At Q8_0 it needs about 0.9 GB. The LattePanda 3 Delta 8GB has 4.0 GB usable.

How fast is GLiGuard 300M on the LattePanda 3 Delta 8GB?

It makes one decision in about 352 ms 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.