Ling 3.0 Flash
Active Parameters
124B
Context Length
262K
Modality
Text
Architecture
Mixture of Experts (MoE)
License
MIT License
Release Date
2 Aug 2026
Knowledge Cutoff
-
API Pricing (per 1M)
Input: $0.02 · Output: $0.06
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
13x RTX 4090
24GB VRAM
Datacenter
4x NVIDIA A100
80GB VRAM
Apple Silicon
3x Apple M3 Max
128GB VRAM
262,144 tokens
Consumer
24x RTX 4090
24GB VRAM
Datacenter
7x NVIDIA A100
80GB VRAM
Apple Silicon
5x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
No evaluation benchmarks for Ling 3.0 Flash available.
Rankings
Overall Rank
-
Coding Rank
-
About Ling 3.0 Flash
Ling 3.0 Flash is an open-weight 124B-parameter mixture-of-experts model activating approximately 5.1B parameters per token. Developed by InclusionAI, it focuses on token efficiency and low-latency execution for production agentic applications.
Technical Specifications
Attention
Attention Structure
Multi-Layer Attention
Attention Heads
32
Key-Value Heads
32
Attention Head Dimension
128
Position Embedding
ROPE
RoPE Theta
6,000,000
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
Yes
Linear Attention Ratio
83.3%
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
2,560
Number of Layers
42
FFN Intermediate Size (Dense)
6,144
Multi-Token Prediction Heads
1
Tokenizer
Vocabulary Size
157,184
Mixture of Experts
Total Expert Parameters
5.1B
Number of Experts
512
Active Experts
8
Shared Experts
1
FFN Intermediate Size (per Expert)
768
Dense Layers Before MoE
2
Resources
About Ling
The Ling model family developed by InclusionAI.
Other Ling Models
- No related models available