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Can the Raspberry Pi 5 (4 GB) run LFM2.5 1.2B Instruct?

30Basic quality (D) · 30 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q4_K_M it needs about 1.6 GB of 2.8 GB usable, with an estimated speed of 15.0 tok/s.

Best quant
Q4_K_M
Generation speed
15.0 tok/s
Prompt processing
76.9 tok/s
First token (1K prompt)
13.3 s
Largest safe context
100K

Best app to run it on the Raspberry Pi 5 (4 GB)

On Linux on ARM boards

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Point-and-click: search, download and chat in one window.

Open LFM2.5 1.2B Instruct in LM Studio

  • llama.cpp Usually the first to support new model architectures.
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

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 1.2B Instruct

Recommended for the Raspberry Pi 5 (4 GB): 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/LiquidAI/LFM2.5-1.2B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct (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 8 hours ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 30Basic quality (D) · 30 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 61/100 (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 well
How well it runs on the Raspberry Pi 5 (4 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.13.7 / 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.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.9.2 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.13.5 / 15
Match score61.4 / 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 Raspberry Pi 5 (4 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 1.4 GB 2.8 GB 24.6 tok/s Runs well 22Basic quality (D) · 22 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q3_K_M 1.5 GB 2.8 GB 19.3 tok/s Runs well 26Basic quality (D) · 26 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q4_K_M 1.6 GB 2.8 GB 15.0 tok/s Runs well 30Basic quality (D) · 30 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 1.8 GB 2.8 GB 12.4 tok/s Runs well 31Basic quality (D) · 31 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 1.9 GB 2.8 GB 10.4 tok/s Runs well 31Basic quality (D) · 31 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 2.2 GB 2.8 GB 7.6 tok/s Slow 32Basic quality (D) · 32 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 3.3 GB 2.8 GB 3.0 tok/s Very slowfar over memory limit 32Basic quality (D) · 32 of 100: how capable the model is at F16, whatever it runs on. Match score 37/100 (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

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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