Parameters
7B
Context Length
131K
Modality
Text
Architecture
Dense
KV Cache Rate
36 KB/tok
License
Apache 2.0
Release Date
27 Dec 2024
Knowledge Cutoff
-
API Pricing (per 1M)
Self-hosted only
Precision
Context
Workload Memory: 9.76 GB
INT4 · 8K context
Base Weights: 3.26 GB
KV Cache: 4.00 GB
Runtime & Graphs: 2.50 GB
1x NVIDIA L4 24GB
9.8 / 21.6 GB usable
1x RTX 3060 12GB
9.8 / 10.8 GB usable
Apple M4 24GB
9.8 / 18.0 GB usable
Determine requirements for your specific workload:
Open in VRAM Calculator
Rank
#209
| Benchmark | Score | Rank |
|---|---|---|
Graduate-Level QA | 0.491 | 105 |
Overall Rank
#209
Coding Rank
-
DeepSeek-R1 7B is a dense reasoning model distilled from DeepSeek-R1 onto the Qwen2.5-Math-7B foundation. It specializes in mathematical theorem proving, algorithmic problem solving, and structured logic over a 131K context window.
Attention
Attention Structure
Multi-Layer Attention
Attention Heads
64
Key-Value Heads
64
Position Embedding
ROPE
RoPE Theta
10,000
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
4,096
Number of Layers
32
FFN Intermediate Size (Dense)
18,944
Tokenizer
Vocabulary Size
152,064
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.