Parameters
70B
Context Length
33K
Modality
Text
Architecture
Dense
KV Cache Rate
90 KB/tok
License
MIT License
Release Date
27 Dec 2024
Knowledge Cutoff
-
API Pricing (per 1M)
Input: $0.70 · Output: $1.10
Precision
Context
Workload Memory: 70.10 GB
INT4 · 8K context
Base Weights: 32.60 GB
KV Cache: 35.00 GB
Runtime & Graphs: 2.50 GB
1x NVIDIA H100 80GB
70.1 / 72.0 GB usable
4x RTX 4090 24GB
70.1 / 86.4 GB usable · TP 4
Apple M4 Max 128GB
70.1 / 96.0 GB usable
Determine requirements for your specific workload:
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Rank
#162
| Benchmark | Score | Rank |
|---|---|---|
Graduate-Level QA | 0.652 | 92 |
Intelligence Index | 0.08 | 254 |
Overall Rank
#162
Coding Rank
-
DeepSeek-R1 70B is a dense reasoning foundation model distilled from DeepSeek-R1 onto the Llama-3.3-70B-Instruct architecture. It combines deep logical inference and competitive programming skills with efficient enterprise deployment profiles.
Attention
Attention Structure
Multi-Layer Attention
Attention Heads
112
Key-Value Heads
112
Attention Head Dimension
128
Position Embedding
ROPE
RoPE Theta
500,000
Normalization
RMS Normalization
Activation Function
Swish
Dimensions
Hidden Dimension Size
8,192
Number of Layers
80
FFN Intermediate Size (Dense)
28,672
Tokenizer
Vocabulary Size
128,256
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.