Liquid D1-Omni 600M
Parameters
600M
Context Length
16K
Modality
Text
Architecture
Dense
License
other
Release Date
5 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
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
16,384 tokens
Consumer
1x 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 Liquid D1-Omni 600M available.
Rankings
Overall Rank
-
Coding Rank
-
About Liquid D1-Omni 600M
Liquid D1-Omni 600M is a compact multimodal omni model developed by Liquid AI designed for efficient edge deployment across audio, text, and visual inputs. It leverages liquid neural network architectures for real-time streaming and on-device processing.
Technical Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
16
Key-Value Heads
8
Attention Head Dimension
64
Position Embedding
ROPE
RoPE Theta
1,000,000
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
Yes
Linear Attention Ratio
62.5%
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Auxiliary Parameters
-
Hidden Dimension Size
1,024
Number of Layers
16
FFN Intermediate Size (Dense)
6,656
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
65,536
Resources
About LFM2
The LFM2 model family developed by Liquid AI.