LFM2.5 24B A2B
Active Parameters
24B
Context Length
131K
Modality
Text
Architecture
Mixture of Experts (MoE)
License
other
Release Date
12 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
Consumer
3x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
131,072 tokens
Consumer
3x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
No evaluation benchmarks for LFM2.5 24B A2B available.
Rankings
Overall Rank
-
Coding Rank
-
About LFM2.5 24B A2B
LFM2.5 24B A2B is a mixture-of-experts model from Liquid AI activating 2 billion parameters per token. It leverages Liquid's non-transformer dynamical architecture designed for high efficiency and extended sequence modeling.
Technical Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
32
Key-Value Heads
8
Attention Head Dimension
64
Position Embedding
ROPE
RoPE Theta
10,000,000
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
Yes
Linear Attention Ratio
75.0%
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Auxiliary Parameters
-
Hidden Dimension Size
2,048
Number of Layers
40
FFN Intermediate Size (Dense)
11,776
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
128,000
Mixture of Experts
Total Expert Parameters
2.5B
Number of Experts
64
Active Experts
4
Shared Experts
-
FFN Intermediate Size (per Expert)
1,536
Dense Layers Before MoE
2
Resources
About LFM2
The LFM2 model family developed by Liquid AI.