on

Can the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (192 GB) run LFM2.5 230M?

10Basic quality (D) · 10 of 100: how capable the model is at F16, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At F16 it needs about 15.0 GB of 173 GB usable, with an estimated speed of 93.5 tok/s.

Best quant
F16
Generation speed
93.5 tok/s
Prompt processing
2,087 tok/s
First token (1K prompt)
0.5 s
Largest safe context
125K

Best app to run it on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (192 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Usually the first to support new model architectures.
llama-server -hf LiquidAI/LFM2.5-230M-GGUF:BF16

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 LFM2.5 230M

Recommended for the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (192 GB): F16. 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 F16 file (opens in a new tab)

huggingface.co/LiquidAI/LFM2.5-230M-GGUF (opens in a new tab)

ollama run hf.co/LiquidAI/LFM2.5-230M-GGUF:BF16

Original weights · Hugging Face

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

huggingface.co/LiquidAI/LFM2.5-230M (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 10Basic quality (D) · 10 of 100: how capable the model is at F16, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at F16, 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 great
How well it runs on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (192 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.4.5 / 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.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.2.0 / 15
Match score46.5 / 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 5 225 / 7 265 / 9 285 (desktop) NAS (192 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 14.6 GB 173 GB 654 tok/s Runs great 7Basic quality (D) · 7 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q3_K_M 14.6 GB 173 GB 515 tok/s Runs great 8Basic quality (D) · 8 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q4_K_M 14.6 GB 173 GB 399 tok/s Runs great 9Basic quality (D) · 9 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 14.7 GB 173 GB 329 tok/s Runs great 9Basic quality (D) · 9 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 14.7 GB 173 GB 276 tok/s Runs great 9Basic quality (D) · 9 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 14.8 GB 173 GB 203 tok/s Runs great 10Basic quality (D) · 10 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 15.0 GB 173 GB 93.5 tok/s Runs great 10Basic quality (D) · 10 of 100: how capable the model is at F16, whatever it runs on. Match score 46/100 (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 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does LFM2.5 230M need on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 192GB?

At F16 it needs about 15.0 GB. The Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 192GB has 173 GB usable.

How fast is LFM2.5 230M on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 192GB?

About 93.5 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of LFM2.5 230M should I use on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 192GB?

F16. 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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