on

Can the Other Android device with Dimensity 9400 or 9400+ (16 GB RAM) run LFM2.5 8B-A1B?

49Fair quality (C) · 49 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q4_K_M it needs about 5.9 GB of 9.9 GB usable, with an estimated speed of 43.8 tok/s.

Best quant
Q4_K_M
Generation speed
43.8 tok/s
Prompt processing
165 tok/s
First token (1K prompt)
6.2 s
Largest safe context
125K

Best app to run it on the Other Android device with Dimensity 9400 or 9400+ (16 GB RAM)

On Android

PocketPal AI

Mobile app · runs on llama.cpp

  • Runs the model on the device itself, offline: download it once in the app.

Get it on Google Play Then download LFM2.5 8B-A1B in the app

  • ChatterUI Runs the model on the device itself, offline: download it once in the app.

Want an agent? LFM2.5 8B-A1B supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

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 LFM2.5 8B-A1B

On a phone, tablet or other mobile device you install a chat app and get the model file through it, so there are no commands to copy. Use LFM2.5-8B-A1B-Q4_K_M.gguf for the Other Android device with Dimensity 9400 or 9400+ (16 GB RAM).

On Android

PocketPal AI

Free mobile app · runs the model on the device with llama.cpp

Get it on Google Play

  1. Install PocketPal AI (free) and open it.
  2. Tap the menu (☰), then Models, then + and Add from Hugging Face.
  3. Search for LiquidAI/LFM2.5-8B-A1B-GGUF, open it and download LFM2.5-8B-A1B-Q4_K_M.gguf.
  4. Tap Load next to the model and start a chat. It runs on the device, with no connection needed from then on.

Also works: ChatterUI

Files and commands for a computer

Recommended for the Other Android device with Dimensity 9400 or 9400+ (16 GB RAM): Q4_K_M. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run lfm2.5:8b-a1b-q4_K_M

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/LiquidAI/LFM2.5-8B-A1B-GGUF (opens in a new tab)

ollama run hf.co/LiquidAI/LFM2.5-8B-A1B-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/LiquidAI/LFM2.5-8B-A1B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/Liquid4All/cookbook (opens in a new tab)

Liquid AI’s official cookbook for running LFM models.

git clone https://github.com/Liquid4All/cookbook

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

How it’s rated

Quality 49Fair quality (C) · 49 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 75/100 (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 Runs great
How well it runs on the Other Android device with Dimensity 9400 or 9400+ (16 GB RAM): 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.22.2 / 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.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.13.6 / 15
Match score75.9 / 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 Other Android device with Dimensity 9400 or 9400+ (16 GB RAM)

Quant Memory needed Available Speed Runs Quality File to pick in the app
Q2_K 4.1 GB 9.9 GB 66.8 tok/s Runs great 36Basic quality (D) · 36 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. —
Q3_K_M 4.9 GB 9.9 GB 54.7 tok/s Runs great 43Fair quality (C) · 43 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. —
Q4_K_M 5.9 GB 9.9 GB 43.8 tok/s Runs great 49Fair quality (C) · 49 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. Q4_K_M
Q5_K_M 6.7 GB 9.9 GB 37.0 tok/s Runs great 50Fair quality (C) · 50 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. Q5_K_M
Q6_K 7.6 GB 9.9 GB 31.6 tok/s Runs great 51Fair quality (C) · 51 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. Q6_K
Q8_0 9.7 GB 9.9 GB 23.8 tok/s Runs welltight fit 52Fair quality (C) · 52 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. Q8_0
F16 17.6 GB 9.9 GB — Won't runover the app memory limit 52Fair quality (C) · 52 of 100: how capable the model is at F16, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking. BF16

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 8B-A1B need on the Dimensity 9400 or 9400+ 16GB?

At Q4_K_M it needs about 5.9 GB. The Dimensity 9400 or 9400+ 16GB has 9.9 GB usable.

How fast is LFM2.5 8B-A1B on the Dimensity 9400 or 9400+ 16GB?

About 43.8 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of LFM2.5 8B-A1B should I use on the Dimensity 9400 or 9400+ 16GB?

Q4_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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