DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

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A Strong, Economical, and Efficient Mixture-of-Experts Language Model

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Inputs: textOutputs: text

Type

Open Source

Company

DeepSeek-AI

Visit Website GitHub (38 stars)

About DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

DeepSeek-V2 is an open-source Mixture-of-Experts (MoE) language model developed by DeepSeek-AI, designed to achieve strong performance with economical training and efficient inference. The model has 236 billion total parameters, with 21 billion activated per token, and supports a context length of 128,000 tokens. It introduces two innovative architectures: Multi-head Latent Attention (MLA), which compresses the key-value cache into a latent vector to drastically reduce memory usage and accelerate inference, and DeepSeekMoE, which enables sparse computation to lower training costs. Pretrained on 8.1 trillion tokens of high-quality, multi-source data, the model undergoes supervised fine-tuning (SFT) and reinforcement learning (RL) to further enhance its capabilities. Compared to its predecessor DeepSeek 67B, DeepSeek-V2 saves 42.5% of training costs, reduces the KV cache by 93.3%, and increases maximum generation throughput by 5.76 times. Despite having only 21B activated parameters, DeepSeek-V2 achieves top-tier performance among open-source models across various benchmarks.

Key Features

Mixture-of-Experts architecture with 236B total parameters, 21B activated per token

Multi-head Latent Attention (MLA) compresses KV cache into a latent vector for efficient inference

DeepSeekMoE enables sparse computation to reduce training costs

Supports 128K token context length

Pretrained on 8.1 trillion tokens of high-quality multi-source data

Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) fine-tuning

42.5% training cost savings and 93.3% KV cache reduction compared to DeepSeek 67B

5.76x maximum generation throughput improvement over DeepSeek 67B

Pros & Cons

Pros

Top-tier performance among open-source models despite only 21B activated parameters
Highly efficient inference due to KV cache compression via Multi-head Latent Attention
Economical training achieved through sparse Mixture-of-Experts computation
Long context support of 128K tokens
Open-source and freely available for use and modification

Cons

Large total parameter count (236B) may require substantial memory and computational resources to serve the full model
Limited information on specific benchmark details and comparisons beyond the abstract

Best For

General text generation and completionConversational AI (chat versions available)Natural language understanding tasksResearch and experimentation in open-source LLMsApplications requiring long context up to 128K tokens

FAQ

What is the architecture of DeepSeek-V2?

DeepSeek-V2 uses a Mixture-of-Experts (MoE) architecture with two key innovations: Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA compresses the key-value cache into a latent vector to reduce memory and accelerate inference, while DeepSeekMoE enables sparse computation for cost-effective training.

How many parameters does DeepSeek-V2 have?

The model has 236 billion total parameters, with 21 billion activated for each token processed.

What context length does DeepSeek-V2 support?

DeepSeek-V2 supports a context length of 128,000 tokens.

How does DeepSeek-V2 compare to the previous DeepSeek 67B model?

Compared to DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance while saving 42.5% of training costs, reducing the KV cache by 93.3%, and boosting maximum generation throughput by 5.76 times.

Is DeepSeek-V2 open-source?

Yes, DeepSeek-V2 is an open-source language model freely available.