DeepSeek-R1 3B
Parameters
3B
Context Length
33K
Modality
Text
Architecture
Dense
License
Llama 3.2 Community License
Release Date
27 Dec 2024
Knowledge Cutoff
-
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
32,768 tokens
Consumer
2x 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 DeepSeek-R1 3B available.
Rankings
Overall Rank
-
Coding Rank
-
About DeepSeek-R1 3B
DeepSeek-R1 3B is a lightweight distilled reasoning model built upon the Llama 3.2 architecture. It delivers structured chain-of-thought problem-solving with minimal computational overhead on resource-constrained devices.
Technical Specifications
Attention
Attention Structure
Multi-Layer Attention
Attention Heads
48
Key-Value Heads
48
Attention Head Dimension
-
Position Embedding
ROPE
RoPE Theta
10,000
Sliding Window Attention
Yes
Sliding Window Size
4,096
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
3,072
Number of Layers
32
FFN Intermediate Size (Dense)
18,944
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.