DeepSeek-R1 14B
Parameters
14B
Context Length
131K
Modality
Text
Architecture
Dense
License
MIT License
Release Date
27 Dec 2024
Knowledge Cutoff
Jul 2024
API Pricing (per 1M)
Self-hosted only
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
2x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
131,072 tokens
Consumer
13x RTX 4090
24GB VRAM
Datacenter
4x NVIDIA A100
80GB VRAM
Apple Silicon
3x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
Rank
#142
| Benchmark | Score | Rank |
|---|---|---|
Graduate-Level QA | 0.591 | 82 |
Intelligence Index | 0.08 | 185 |
Rankings
Overall Rank
#142
Coding Rank
-
About DeepSeek-R1 14B
DeepSeek-R1 14B is a dense reasoning model distilled from DeepSeek-R1 onto the Qwen2.5-14B backbone. It delivers competitive multi-step mathematical reasoning and programming synthesis across a 131K token context window.
Technical Specifications
Attention
Attention Structure
Multi-Layer Attention
Attention Heads
80
Key-Value Heads
80
Attention Head Dimension
-
Position Embedding
ROPE
RoPE Theta
1,000,000
Sliding Window Attention
No
Sliding Window Size
131,072
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
5,120
Number of Layers
40
FFN Intermediate Size (Dense)
13,824
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
152,064
About DeepSeek-R1
DeepSeek-R1 is a model family developed for logical reasoning tasks. It incorporates a Mixture-of-Experts architecture for computational efficiency and scalability. The family utilizes Multi-Head Latent Attention and employs reinforcement learning in its training, with some variants integrating cold-start data.