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LFM3 VL 2B

Parameters

2B

Context Length

128K

Modality

Text

Architecture

Dense

License

other

Release Date

19 Oct 2026

Knowledge Cutoff

-

API Pricing (per 1M)

Self-hosted only

System Requirements

VRAM requirements for different quantization methods and context sizes

1,024 tokens

5.46 GB VRAM

Consumer

1x RTX 4090

24GB VRAM

Datacenter

1x NVIDIA A100

80GB VRAM

Apple Silicon

1x Apple M3 Max

128GB VRAM

128,000 tokens

11.56 GB VRAM

Consumer

1x RTX 4090

24GB VRAM

Datacenter

1x NVIDIA A100

80GB VRAM

Apple Silicon

1x Apple M3 Max

128GB VRAM

Architecture Diagram

Input TokensToken EmbeddingPosition: RoPEHidden: 2k · Context: 128K · Vocab: 128kx 24 layersRMSNormPre-AttentionGrouped-Query Attention16Q / 4KV heads · SW: 256Head dim: 128+RMSNormPre-FFNFeed-Forward NetworkSwiGLUIntermediate: 7.7k+Final RMSNormOutput Logits

Evaluation Benchmarks

No evaluation benchmarks for LFM3 VL 2B available.

Rankings

Overall Rank

-

Coding Rank

-

About LFM3 VL 2B

LFM3 VL 2B is an efficient vision-language model designed by Liquid AI for on-device multimodal reasoning and image understanding. It integrates visual encoders with the liquid foundational architecture to maximize inference efficiency.

Technical Specifications

Attention

Attention Structure

Grouped-Query Attention

Attention Heads

16

Key-Value Heads

4

Attention Head Dimension

128

Position Embedding

ROPE

RoPE Theta

1,000,000

Sliding Window Attention

Yes

Sliding Window Size

256

Sliding Window Ratio

20.8%

Linear Attention

Yes

Linear Attention Ratio

54.2%

Normalization

RMS Normalization

Activation Function

SwigLU

Dimensions

Hidden Dimension Size

2,048

Number of Layers

24

FFN Intermediate Size (Dense)

7,680

Tokenizer

Vocabulary Size

128,000

About LFM3

The LFM3 model family developed by Liquid AI.


Other LFM3 Models