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Can the Arduino Portenta X8 (2 GB) run NuExtract 2.0 4B?

72Great quality (A) · 72 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.

No, it doesn’t fit

No. NuExtract 2.0 4B needs about 3.1 GB even at Q4_K_M; the Arduino Portenta X8 (2 GB) has 1.2 GB usable.

Download NuExtract 2.0 4B

Recommended: Q4_K_M. 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 Q4_K_M file (opens in a new tab)

huggingface.co/numind/NuExtract-2.0-4B-GGUF (opens in a new tab)

ollama run hf.co/numind/NuExtract-2.0-4B-GGUF:Q4_K_M

Original weights · Hugging Face

Full-precision files from NuMind, for fine-tuning, vLLM, Transformers.

huggingface.co/numind/NuExtract-2.0-4B (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 72Great quality (A) · 72 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_K_M, 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 Won't run
How well it runs on the Arduino Portenta X8 (2 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.9.7 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.0.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.0.0 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.0.0 / 15
Match score9.7 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every quantization on the Arduino Portenta X8 (2 GB)

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 3.1 GB 1.2 GB — Won't run 72Great quality (A) · 72 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 3.4 GB 1.2 GB — Won't run 73Great quality (A) · 73 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 3.8 GB 1.2 GB — Won't run 75Great quality (A) · 75 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 4.7 GB 1.2 GB — Won't run 76Great quality (A) · 76 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 8.3 GB 1.2 GB — Won't run 76Great quality (A) · 76 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 256-token input and about 24 tokens written. Batching servers process many requests at once.

Hardware that can run it

Models that do run on the Arduino Portenta X8 (2 GB)

Quick answers

How much memory does NuExtract 2.0 4B need on the Arduino Portenta X8 2GB?

Even at Q4_K_M it needs about 3.1 GB; the Arduino Portenta X8 2GB has 1.2 GB usable.

What can the Arduino Portenta X8 2GB run instead?

Llama Prompt Guard 2 86M, mxbai-rerank-base v2 and GLiClass ModernBERT Large v3 run best on it; the page lists more, each with its speed.

What hardware can run NuExtract 2.0 4B?

RTX 5090 32GB, NVIDIA RTX PRO 6000 Blackwell 96GB and RTX 4090 24GB run it; the page lists more, each with its speed.

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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