LemonDecider 4B on Intel Core Ultra 200V (Arc 140V) (32 GB)

Can the Intel Core Ultra 200V (Arc 140V) (32 GB) run LemonDecider 4B?

Yes, it runs well

Runs well. At Q8_0 it needs about 6.5 GB of 18.2 GB usable and makes one decision in about 291 ms.

Best precision: Q8_0
Time per decision: 291 ms
Decisions per second: 3.4/s
Input assumed: 256 tokens
Returns: Probability or score

Best app to run it on the Intel Core Ultra 200V (Arc 140V) (32 GB)

On Windows

Sentence Transformers

Python library · runs on PyTorch (Transformers)

Can only use the CPU on this hardware, so it will be slow.
A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks.

python -c "from transformers import pipeline; print(pipeline('text-classification', model='inclusionAI/LemonDecider')('text'))"

On Linux

Sentence Transformers

Python library · runs on PyTorch (Transformers)

Can only use the CPU on this hardware, so it will be slow.
A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks.

python -c "from transformers import pipeline; print(pipeline('text-classification', model='inclusionAI/LemonDecider')('text'))"

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 LemonDecider 4B

Original weights · Hugging Face

Full-precision files from Ant Group (inclusionAI), for Transformers, Sentence Transformers, Text Embeddings Inference, Infinity or vLLM.

huggingface.co/inclusionAI/LemonDecider (opens in a new tab)

No sign-in needed.

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 84/100 (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 Intel Core Ultra 200V (Arc 140V) (32 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.	36.0 / 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.	8.8 / 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	84.8 / 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 Intel Core Ultra 200V (Arc 140V) (32 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q8_0`	6.5 GB	18.2 GB	291 ms	Runs well	80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`F16`	10.8 GB	18.2 GB	334 ms	Runs well	80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Times are for one request with a 256-token input. Batching servers process many requests at once.

Other hardware that runs it

80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX F16 · 23 ms · Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT F16 · 28 ms · Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. AMD Ryzen AI Max+ 395 (128 GB) F16 · 43 ms · Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. Apple M2 Ultra (192 GB) F16 · 90 ms · Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. Apple M3 Ultra (256 GB) F16 · 87 ms · Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. Apple M3 Ultra (512 GB) F16 · 87 ms · Runs great

Quick answers

How much memory does LemonDecider 4B need on the Intel Core Ultra 200V (Arc 140V) 32GB?

At Q8_0 it needs about 6.5 GB. The Intel Core Ultra 200V (Arc 140V) 32GB has 18.2 GB usable.

How fast is LemonDecider 4B on the Intel Core Ultra 200V (Arc 140V) 32GB?

It makes one decision in about 291 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.