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LFM3 1.7B

Parameters

1.7B

Context Length

262K

Modality

Text

Architecture

Dense

License

other

Release Date

23 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

4.87 GB VRAM

Consumer

1x RTX 4090

24GB VRAM

Datacenter

1x NVIDIA A100

80GB VRAM

Apple Silicon

1x Apple M3 Max

128GB VRAM

262,144 tokens

17.42 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: 262K · 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 1.7B available.

Rankings

Overall Rank

-

Coding Rank

-

About LFM3 1.7B

LFM3 1.7B is a third-generation liquid foundation model designed for ultra-efficient on-device edge deployment. It features dynamic state-space and non-transformer hybrid architectures for high throughput and minimal memory usage.

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
  • No related models available