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DeepSeek-R1 7B

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

Hardware Sizing

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

Minimum Required Hardware

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

Architecture Diagram

Input TokensToken EmbeddingPosition: RoPEHidden: 4.1k · Context: 131K · Vocab: 152.1kx 32 layersRMSNormPre-AttentionMulti-Layer Attention64Q / 64KV headsHead dim: 64+RMSNormPre-FFNFeed-Forward NetworkSwiGLUIntermediate: 18.9k+Final RMSNormOutput Logits

Evaluation Benchmarks

Rank

#209

BenchmarkScoreRank

Graduate-Level QA

GPQA

0.491

105

Rankings

Overall Rank

#209

Coding Rank

-

About DeepSeek-R1 7B

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.

Technical Specifications

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

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.


Other DeepSeek-R1 Models