Instructions to use LiquidAI/LFM2.5-VL-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-VL-3B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="LiquidAI/LFM2.5-VL-3B", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("LiquidAI/LFM2.5-VL-3B", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("LiquidAI/LFM2.5-VL-3B", trust_remote_code=True, device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LiquidAI/LFM2.5-VL-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2.5-VL-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2.5-VL-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-VL-3B

SGLang

How to use LiquidAI/LFM2.5-VL-3B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "LiquidAI/LFM2.5-VL-3B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2.5-VL-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "LiquidAI/LFM2.5-VL-3B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2.5-VL-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM2.5-VL-3B with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2.5-VL-3B
```

LFM2.5-VL-3B / README.md

samuelstevens

Add link to playground.

b6855f3 about 19 hours ago

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	---
	library_name: transformers
	license: other
	license_name: lfm1.0
	license_link: LICENSE
	language:
	- ar
	- zh
	- en
	- fr
	- de
	- hi
	- id
	- it
	- ja
	- ko
	- pl
	- pt
	- ru
	- es
	- th
	- vi
	pipeline_tag: image-text-to-text
	tags:
	- liquid
	- lfm2.5
	- edge
	---

	<div align="center">
	<img
	src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
	alt="Liquid AI"
	style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
	/>
	<div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
	<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> •
	<a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> •
	<a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> •
	<a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
	</div>
	</div>

	# LFM2.5-VL-3B

	LFM2.5-VL-3B is a multimodal variant of LFM2.5, a family of hybrid models designed for on-device deployment. It builds on LFM2-VL-3B with further mid- and post-training. LFM2.5-VL-3B can process both text and images, and uses the LFM2.5-2.6B language model as its backbone, combined with a SigLIP2 NaFlex vision encoder.

	* Better grounding: Improved grounding and object detection with natural language queries.
	* Better OCR: Full page OCR with layout annotation. See [layout annotation format](#layout-annotation-format) for more information.
	* Efficient inference: 228 tok/s on an Apple M5 Max and 116 tok/s on an AMD Ryzen AI Max+ 395, in under 3.3 GB of memory.

	Find more information about LFM2.5-VL-3B in our [release post](https://www.liquid.ai/blog/lfm2-5-vl-3b).

	![lfm2_5_vl_3b_task_group_averages](https://cdn-uploads.huggingface.co/production/uploads/644249b08443bce4c9890a0f/xw2m32B8IA0mRbhn_KG7f.png)

	> [!NOTE]
	> 💻 Demos: Try LFM2.5-VL-3B's vision understanding capabilities in a Hugging Face space without any setup:
	> [Vision-capable chat in your browser](https://huggingface.co/spaces/LiquidAI/LFM2.5-VL-3B-WebGPU): allows you to upload images or use the webcam to capture images and let the model interact with them, as well as use tool calls and display generated bounding boxes. If you just want to chat about images, the [LiquidAI playground](http://playground.liquid.ai/chat?model=lfm2.5-vl-3b) is a fast way to do that.

	## Model Details

	\| Model \| Description \|
	\|-------\|-------------\|
	\| [LFM2.5‑VL‑3B](https://huggingface.co/LiquidAI/LFM2.5-VL-3B) \| Original checkpoint in native format. Best for fine-tuning and inference with HF Transformers, vLLM and SGLang \|
	\| [LFM2.5‑VL‑3B‑GGUF](https://huggingface.co/LiquidAI/LFM2.5-VL-3B-GGUF) \| Quantized GGUF exports of the original checkpoint. Best for CPU inference with reduced memory usage with llama.cpp \|
	\| [LFM2.5‑VL‑3B‑ONNX](https://huggingface.co/LiquidAI/LFM2.5-VL-3B-ONNX) \| Quantized ONNX exports for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). See the [demo](https://huggingface.co/spaces/LiquidAI/LFM2.5-VL-3B-WebGPU). \|
	\| [LFM2.5-VL-3B-MLX](https://huggingface.co/LiquidAI/LFM2.5-VL-3B-MLX-8bit) \| Quantized MLX exports for Apple Silicon. Optimized for fast inference on Mac devices using the [mlx-vlm](https://github.com/Blaizzy/mlx-vlm) framework. \|

	- LM Backbone: LFM2.5-2.6B
	- Vision encoder: SigLIP2 NaFlex shape‑optimized 400M
	- Vocabulary size: 128,000
	- Context length: 32,768 tokens
	- Languages: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish
	- Native resolution processing: Uses SigLIP2's NaFlex; large images are split into non-overlapping 512×512 patches and a resized whole-image thumbnail.
	- Generation parameters:
	- text: `temperature=0.2`, `top_k=50`, `repetition_penalty=1.0`
	- vision: Use the `processor_config.json` file.

	We recommend using it for single-turn, high-throughput, low-latency tasks; for example, for near-realtime object detection in automotive applications, batch processing scanned documents with OCR with layout information for turning PDFs into searchable text, or for on-device translation of menus and road signs into your native language.

	It is not recommended for long-context, reasoning-intensive tasks, such as visual web design, or answering highly technical questions about blueprints.

	### Chat Template

	LFM2.5 uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/key-concepts/chat-template#vision-models) for details. Example:

	```
	<\|startoftext\|><\|im_start\|>system
	You are a helpful assistant trained by Liquid AI.<\|im_end\|>
	<\|im_start\|>user
	What species is in this picture?<image><\|im_end\|>
	<\|im_start\|>assistant
	```

	You can use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#using-applychattemplate) to format your messages automatically.

	> [!TIP]
	> Note: The `apply_chat_template()` method automatically inserts the `<image>` tag for each image in your message. Do not include `<image>` in your message content.

	## Inference

	LFM2.5-VL is supported by many inference frameworks. See the [Inference documentation](https://docs.liquid.ai/lfm/inference/transformers) for the full list.

	\| Name \| Description \| Docs \| Notebook \|
	\|------\|-------------\|------\|----------\|
	\| [Transformers](https://github.com/huggingface/transformers) \| Simple inference with direct access to model internals. \| <a href="https://docs.liquid.ai/lfm/inference/transformers#vision-models">Link</a>\| <a href="https://colab.research.google.com/drive/1WVQpf4XrHgHFkP0FnlZfx2nK8PugvQNZ?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| [vLLM](https://github.com/vllm-project/vllm) \| High-throughput production deployments with GPU. \| <a href="https://docs.liquid.ai/deployment/gpu-inference/vllm#vision-models">Link</a> \| <a href="https://colab.research.google.com/drive/1sUfQlqAvuAVB4bZ6akYVQPGmHtTDUNpF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| [SGLang](https://github.com/sgl-project/sglang) \| High-throughput production deployments with GPU. \| <a href="https://docs.liquid.ai/deployment/gpu-inference/sglang#vision-models">Link</a> \| <a href="https://colab.research.google.com/drive/1qJlAFag223yFOZGzuMIkYUFhybM9ao5g?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| [llama.cpp](https://github.com/ggml-org/llama.cpp) \| Cross-platform inference with CPU offloading. \| <a href="https://docs.liquid.ai/lfm/inference/llama-cpp#vision-models">Link</a> \| <a href="https://colab.research.google.com/drive/1q2PjE6O_AahakRlkTNJGYL32MsdUcj7b?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|

	### Quick start
	Quick start with Transformers (compatible with `transformers>=5.0.0`):

	You will need `torch`, `transformers`, and `torchvision`.

	```python
	import torch
	from transformers import AutoModelForImageTextToText, AutoProcessor

	model_id = "LiquidAI/LFM2.5-VL-3B"

	model = AutoModelForImageTextToText.from_pretrained(model_id, dtype=torch.bfloat16)
	processor = AutoProcessor.from_pretrained(model_id)

	messages = [
	{
	"role": "user",
	"content": [
	{"type": "image", "url": "https://placecats.com/300/200"},
	{"type": "text", "text": "Describe this image."},
	],
	}
	]

	inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
	).to(model.device)

	with torch.inference_mode():
	output_ids = model.generate(
	**inputs,
	do_sample=True,
	temperature=0.2,
	top_k=50,
	repetition_penalty=1.0,
	max_new_tokens=256,
	)

	generated_ids = output_ids[:, inputs["input_ids"].shape[1] :]
	print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])
	```

	### Tool Use

	LFM2.5-VL-3B supports function calling in four steps:

	1. Function definition: Provide the list of tools as a JSON object in the system prompt, or use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_extras#passing-tools) with `tools=...`.
	2. Function call: By default, LFM2.5 writes Pythonic function calls (a Python list between `<\|tool_call_start\|>` and `<\|tool_call_end\|>` special tokens), as the assistant answer.
	3. Function execution: Execute the call and return the result with the `tool` role.
	4. Final answer: LFM2.5 interprets the tool output and returns a plain-text answer addressing the original prompt.

	See the [Tool Use documentation](https://docs.liquid.ai/lfm/key-concepts/tool-use) for the full guide. Example:

	```
	<\|startoftext\|><\|im_start\|>system
	List of tools: [{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<\|im_end\|>
	<\|im_start\|>user
	What is the current status of candidate ID 12345?<\|im_end\|>
	<\|im_start\|>assistant
	<\|tool_call_start\|>[get_candidate_status(candidate_id="12345")]<\|tool_call_end\|>Checking the current status of candidate ID 12345.<\|im_end\|>
	<\|im_start\|>tool
	[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<\|im_end\|>
	<\|im_start\|>assistant
	The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<\|im_end\|>
	```

	### Layout Annotation Format

	LFM2.5-VL-3B can do OCR with layout annotation. The layout annotation is a list of regions, each with a label, bounding box, and content. The format is:

	```text
	image_index=<n> <label> [xmin, ymin, xmax, ymax]
	<content>

	image_index=<n> <label> [xmin, ymin, xmax, ymax]
	<content>

	image_index=<n> <label> [xmin, ymin, xmax, ymax]
	<content>

	...
	```

	where:

	- `image_index` is the zero-based index of image
	- `<label>` is one of these layout labels:
	- text
	- title
	- list
	- table
	- table_caption
	- table_footnote
	- image
	- image_block
	- image_caption
	- image_footnote
	- chart
	- equation
	- formula_number
	- code
	- code_caption
	- algorithm
	- aside_text
	- ref_text
	- phonetic
	- page_header
	- page_footer
	- page_number
	- page_footnote
	- `[xmin, ymin, xmax, ymax]` are normalized integer coordinates in [0, 1000], same as our grounding format.
	- `<content>` is the region's content:
	- plain text for text regions
	- LaTeX for equations
	- OTSL (Optimized Table Structure Language, introduced [here](https://arxiv.org/pdf/2305.03393) by IBM) for tables
	- a short description for images and charts

	There will be a blank line between each region.

	To prompt the model to generate this structured output, use a system or user prompt that includes this:

	```text
	Parse this document into its layout regions. The pages are provided as images in reading order. For every region, in reading order across all pages, output a header line immediately followed by the region's content:

	image_index=<n> <label> [xmin, ymin, xmax, ymax]
	<content>

	where:
	- image_index is the zero-based index of the page image the region appears on (0 for the first image, 1 for the second, and so on)
	- <label> is one of these layout labels: text, title, list, table, table_caption, table_footnote, image, image_block, image_caption, image_footnote, chart, equation, formula_number, code, code_caption, algorithm, aside_text, ref_text, phonetic, page_header, page_footer, page_number, page_footnote
	- [xmin, ymin, xmax, ymax] are normalized integer coordinates in [0, 1000]
	- <content> is the region's content: plain text for text regions, LaTeX for equations, OTSL for tables, and a short description for images and charts

	Separate each region block with one blank line. Return only the parsed regions.
	```

	Note that the layout annotation format is still experimental: it may change, may be unreliable, and may not be trivial to parse. We encourage users to try it out and provide feedback!

	## Fine-Tuning

	We recommend fine-tuning LFM2.5-VL models for your specific use case to achieve the best results.

	\| Notebook \| Description \| Link \|
	\|-----------\|----------------------------------------------------------------------\|------\|
	\| SFT (Unsloth) \| Supervised Fine-Tuning with LoRA using Unsloth. \| <a href="https://colab.research.google.com/drive/1FaR2HSe91YDe88TG97-JVxMygl-rL6vB?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="https://colab.research.google.com/github/Liquid4All/cookbook/blob/main/finetuning/notebooks/sft_for_vision_language_model.ipynb"></a> \|
	\| SFT (TRL) \| Supervised Fine-Tuning with LoRA using TRL. \| <a href="https://colab.research.google.com/drive/10530_jt_Joa5zH2wgYlyXosypq1R7PIz?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="https://colab.research.google.com/github/Liquid4All/cookbook/blob/main/finetuning/notebooks/sft_for_vision_language_model_with_trl.ipynb"></a>\|

	## Performance

	### Benchmarks

	LFM2.5-VL-3B significantly improves over LFM2-VL-3B in screen understanding, grounding, multi-image input, and tool use:

	\| Benchmark \| LFM2.5‑VL‑3B (3.1B) \| LFM2‑VL‑3B (3.1B) \| Gemma4 E2B (5.1B) \| Gemma4 E4B (8B) \| InternVL 3.5 4B (4.7B) \| Qwen3.5-2B (2.3B) \| Qwen3.5-4B (4.7B) \|
	\|-----------\|--------------------------\|--------------------\|-------------------\|-----------------\|-------------------------\|--------------------\|--------------------\|
	\| ScreenSpot-v2 (avg) \| <ins>80.7</ins> \| - \| 31.1 \| 50.9 \| 84.2 \| 66.5 \| 78.5 \|
	\| RefCOCO (Macro Prec@1) \| <ins>87.9</ins> \| 57.1<br>(<span style="color: red;">-30.8</span>) \| 67.3 \| 72.1 \| 88.9 \| 78.5 \| 86.6 \|
	\| BLINK \| <ins>61.5</ins> \| 50.2<br>(<span style="color: red;">-11.3</span>) \| 51.8 \| 56.4 \| 57.4 \| 59.3 \| 65.0 \|
	\| MuirBench \| <ins>58.3</ins> \| 34.9<br>(<span style="color: red;">-23.4</span>) \| 40.7 \| 48.9 \| 53.4 \| 49.0 \| 67.0 \|
	\| ToolSandBox \| 59.5 \| 26.4<br>(<span style="color: red;">-33.1</span>) \| 56.5 \| <ins>61.6</ins> \| n/a<a href="#fn-internvl-tools" class="footnote-ref" role="doc-noteref"><sup>1</sup></a> \| 47.7 \| 65.0 \|
	\| BFCLv4 \| 32.5 \| 20.5<br>(<span style="color: red;">-12.0</span>) \| 33.2 \| <ins>40.0</ins> \| n/a<a href="#fn-internvl-tools" class="footnote-ref" role="doc-noteref"><sup>1</sup></a> \| 33.9 \| 53.6 \|

	A selection of benchmarks for LFM2.5-VL-3B, including multimodal reasoning, math, and OCR (see our [blog post](https://www.liquid.ai/blog/lfm2-5-vl-3b) for more benchmarks and details):

	\| Benchmark \| LFM2.5‑VL‑3B (3.1B) \| LFM2‑VL‑3B (3.1B) \| Gemma4 E2B (5.1B) \| Gemma4 E4B (8B) \| InternVL 3.5 2B (2.4B) \| InternVL 3.5 4B (4.7B) \| Qwen3.5-2B (2.3B) \| Qwen3.5-4B (4.7B) \|
	\|-----------\|-------------------------\|-------------------\|-------------------\|-----------------\|------------------------\|------------------------\|-------------------\|-------------------\|
	\| MME \| 73.1 \| 73.0 \| 54.9 \| 68.1 \| 73.3 \| 80.8 \| 76.4 \| 79.5 \|
	\| MMStar \| 63.3 \| 57.7 \| 57.9 \| 61.9 \| 57.5 \| 65.3 \| 67.9 \| 73.3 \|
	\| RealWorldQA \| 73.1 \| 71.1 \| 56.2 \| 61.8 \| 61.4 \| 68.6 \| 71.4 \| 76.2 \|
	\| CountBenchQA \| 87.3 \| 92.2 \| 70.8 \| 80.1 \| 70.6 \| 82.5 \| 83.2 \| 86.9 \|
	\| MMMB \| 83.0 \| 81.9 \| 75.7 \| 80.5 \| 76.4 \| 81.5 \| 73.6 \| 83.4 \|
	\| MM-IF Eval \| 60.6 \| 51.4 \| 64.5 \| 66.7 \| 48.4 \| 54.6 \| 52.1 \| 63.8 \|
	\| MathVista \| 68.5 \| 68.5 \| 62.1 \| 52.9 \| 56.6 \| 59.1 \| 68.8 \| 69.7 \|
	\| MMMU Pro \| 30.5 \| 28.7 \| 34.5 \| 39.1 \| 27.4 \| 31.6 \| 43.5 \| 60.9 \|
	\| ChartQA \| 81.3 \| 80.4 \| 43.5 \| 41.9 \| 81.8 \| 86.5 \| 78.3 \| 84.2 \|
	\| OCRBenchv2<a href="#fn-ocrbenchv2" class="footnote-ref" role="doc-noteref"><sup>2</sup></a> \| 47.5 \| 43.9 \| 44.7 \| 48.7 \| 45.5 \| 49.2 \| 48.0 \| 58.8 \|
	\| POPE \| 88.7 \| 89.2 \| 84.0 \| 86.9 \| 87.3 \| 88.9 \| 88.7 \| 86.0 \|

	<p id="fn-internvl-tools">[1]: InternVL 3.5 doesn't support tool use.</p>

	<p id="fn-ocrbenchv2">[2]: English-only subset of OCRBenchv2.</p>

	### On-device Inference

	LFM2.5-VL-3B decodes 228 tokens/s on an Apple M5 Max and 116 tokens/s on an AMD Ryzen AI Max+ 395, and fits in about 3 GB of memory. It even reaches 20 tokens/s on a Galaxy S26 Ultra, so you can run it fully on-device.

	![lfm2_5_vl_3b_on-device_inference_TTFT](https://cdn-uploads.huggingface.co/production/uploads/644249b08443bce4c9890a0f/X_0b2x4XSB32MC5obpCOL.png)

	### GPU Inference

	On a single NVIDIA H100 with vLLM, LFM2.5-VL-3B reaches the highest output throughput of any model we tested, about 11K tokens per second at high concurrency, or nearly 1B tokens per day.

	![lfm2_5_vl_3b_throughput](https://cdn-uploads.huggingface.co/production/uploads/644249b08443bce4c9890a0f/qt4ipuEqxd--SR6CDZx1Y.png)

	Because it answers directly instead of reasoning, LFM2.5-VL-3B is quick to first token on a single H100, reaching about 34 ms on a 5-frame clip.

	![lfm2_5_vl_3b_ttft](https://cdn-uploads.huggingface.co/production/uploads/644249b08443bce4c9890a0f/oez5EIDV4MSTYyRvsFwGl.png)

	## Contact

	- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
	- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).

	## Citation

	```
	@article{liquidAI2026VL3B,
	author = {Liquid AI},
	title = {LFM2.5-VL-3B: A Better and Faster Vision-Language Model for the Edge},
	journal = {Liquid AI Blog},
	year = {2026},
	note = {www.liquid.ai/blog/lfm2-5-vl-3b},
	}
	```