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Can the Intel N150 NAS (32 GB) run OLMoE 1B-7B Instruct?

21Basic quality (D) · 21 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q3_K_M it needs about 6.6 GB of 26.2 GB usable, with an estimated speed of 28.4 tok/s.

Best quant
Q3_K_M
Generation speed
28.4 tok/s
Prompt processing
55.4 tok/s
First token (1K prompt)
18.5 s
Largest safe context
4K

Best app to run it on the Intel N150 NAS (32 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Usually the first to support new model architectures.
llama-server -hf allenai/OLMoE-1B-7B-0125-Instruct-GGUF:Q3_K_M

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download OLMoE 1B-7B Instruct

Recommended for the Intel N150 NAS (32 GB): Q3_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 Q3_K_M file (opens in a new tab)

huggingface.co/allenai/OLMoE-1B-7B-0125-Instruct-GGUF (opens in a new tab)

ollama run hf.co/allenai/OLMoE-1B-7B-0125-Instruct-GGUF:Q3_K_M

Original weights · Hugging Face

Full-precision files from Ai2, for fine-tuning, vLLM, Transformers.

huggingface.co/allenai/OLMoE-1B-7B-0125-Instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Ai2. The weights themselves download from Hugging Face.

github.com/allenai/OLMoE (opens in a new tab)

git clone https://github.com/allenai/OLMoE

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

How it’s rated

Quality 21Basic quality (D) · 21 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q3_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 Runs well
How well it runs on the Intel N150 NAS (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.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.9.8 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.12.6 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.5.8 / 15
Match score53.3 / 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 N150 NAS (32 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 6.0 GB 26.2 GB 35.3 tok/s Runs great 18Basic quality (D) · 18 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 6.6 GB 26.2 GB 28.4 tok/s Runs well 21Basic quality (D) · 21 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 7.5 GB 26.2 GB 22.4 tok/s Runs well 24Basic quality (D) · 24 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 8.1 GB 26.2 GB 18.7 tok/s Runs well 25Basic quality (D) · 25 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 8.9 GB 26.2 GB 15.8 tok/s Runs well 25Basic quality (D) · 25 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 10.6 GB 26.2 GB 11.8 tok/s Runs well 26Basic quality (D) · 26 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 17.1 GB 26.2 GB 5.5 tok/s Slow 26Basic quality (D) · 26 of 100: how capable the model is at F16, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 4K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does OLMoE 1B-7B Instruct need on the Intel N150 NAS 32GB?

At Q3_K_M it needs about 6.6 GB. The Intel N150 NAS 32GB has 26.2 GB usable.

How fast is OLMoE 1B-7B Instruct on the Intel N150 NAS 32GB?

About 28.4 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of OLMoE 1B-7B Instruct should I use on the Intel N150 NAS 32GB?

Q3_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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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