Super Efficient DeepSeek-V2 Rivals LLaMA 3 and Mixtral

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As artificial intelligence (AI) continues changing the world without taking a day off, another potentially disruptive large language model (LLM) arrives from a Chinese start-up.

Meet DeepSeek-V2, which is gaining attention and plaudits with its extreme focus on efficiency — rivaling the current superstars while using a fraction of the processing power.

Although DeepSeek-V2 has a giant neural network, it achieves efficiency by activating only parts at a time to process information and provide answers. This is unlike most other LLMs, reducing compute while achieving results that rival other open-source models such as LLaMA 3 and Mixtral.

Today, Techopedia deep dives into DeepSeek-V2’s unique architecture and discovers whether it is the newest powerhouse in the AI arena.

Key Takeaways

  • DeepSeek-V2 has 236B parameters but only uses 21B parameters at a time — rivaling models like LLaMA 3 and Mixtral but at less cost.
  • The LLM features Multi-Head Latent Attention (MLA) and a Mixture of Experts (MoE) technique, which allows it to be smarter and more efficient in its processing.
  • The model shone when it came to Chinese language benchmarks, surpassing LLaMA 3 and Mixtral in this area, and achieves high marks in English language tests too.
  • As more LLMs arrive and aim to distinguish themselves and pick up skills from each other, DeepSeek-V2 aims specifically at doing more with less.

What is DeepSeek-V2?

DeepSeek-V2 is an LLM focused on efficiency that was launched in May 2024. Its unique selling point comes from its architecture: although it has 236 billion parameters, it only uses 21 billion of them at a time when processing each token.

Parameters are the number of connections in a network and are analogous to the number of synapses in biological neural networks like our brains.


The human brain has about 100 trillion synapses and one suggestion is that the closer we get to networks as complex as the brain, the closer we get to artificial general intelligence (AGI).


However, a large number of parameters means that the models have a significant computational cost, as large amounts of data need to undergo several stages of processing — meaining intensive computing power over a long length of time, access to expensive GPUs, and time spent collecting and preparing training data.

Therefore, by activating 21B parameters per token, DeepSeek-V2 seeks to be more efficient than most other LLMs, which activate their entire neural network (all their parameters) to provide their responses.

For comparison:

  • LLaMA 3 (8x22B): 70B total parameters, 70B parameters activated during inference.
  • Mixtral (72B): 141B total parameters, 39B parameters activated during inference.

DeepSeek-V2’s Innovative Features

How does the LLM achieve this efficiency?

DeepSeek-V2 has two specific features that help it reduce the number of parameters activated per token:

Multi-Head Latent Attention (MLA): This function allows the model to compress all the detailed information into a smaller organized list, ensuring that the model has a compact, easy-to-use summary of all the important information it needs, instead of sifting through a lot of scattered details. This means it can understand and generate text much more quickly and accurately.

DeepSeekMoE: Mixture of Experts (MoE) is a machine learning technique where different specialized parts of the model called “expert networks” are used to handle specific parts of a problem. MoE allows the AI to be smarter and more efficient by using the right “expert” for each job, saving time and resources while getting the best results.

Key Benefits and Comparisons

How does DeepSeek-V2 perform in terms of benchmarks?

Here we outline and compare its performance compared to other popular models, such as LLaMa 3 and Mixtral, providing values for its overall performance across various benchmarks:

Summary of Comparisons

Essentially, DeepSeek-V2 is very competitive with the above models, especially since it activates fewer parameters per token compared to both of those models. Here’s an overview:

  • English Language Tasks: In this category, DeepSeek-V2 is very comparable to Mixtral and LLaMA 3. It actually surpassed both when it comes to one of the reasoning benchmarks (AGIEval — 51.2% accuracy), but it mostly came in second to LLaMA.
  • Coding Tasks: Again, DeepSeek-V2 performed well compared to the other two models. It came in first on one of the code understanding benchmarks (CRUXEval-I (Acc.) – 52.8%) and second for code synthesis and generation.
  • Math Problems: This is clearly one of its stronger domains. It came in first for two out of the three benchmarks, far surpassing the other two models for one of the benchmarks (CMath (EM) – 78.7% accuracy, compared with Mixtral at 72.3% and LLaMa 3 at 73.9%)
  • Chinese Language Tasks: This is where the LLM really shone, surpassing the other models in every single benchmark (and often by leaps and bounds).

Ultimately, DeepSeek-V2 stands out for its efficiency and performance across multiple benchmarks, particularly in Chinese language tasks. By activating fewer parameters per token, it achieves impressive results while using fewer resources, positioning DeepSeek-V2 as a formidable competitor to models such as LLaMA and Mixtral.

The Bottom Line

DeepSeek-V2 is a promising entrant in the AI market, offering competitive performance alongside increased efficiency and reduced costs. Its architecture allows it to activate fewer parameters per input, but despite this, it rivals competitors like LLaMa 3 and Mixtral in numerous benchmarks.

It is particularly strong in Chinese language tasks and solving math problems, but it is still very adept in English language and coding tasks. Thus, it sets a new standard for AI technology and showcases China’s advancements in this arena.

Ultimately, DeepSeek-V2 is positioning itself as a formidable contender in the AI landscape, pushing the boundaries of what LLMs can achieve.


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Maria Webb
Technology Journalist
Maria Webb
Technology Journalist

Maria is a technology journalist with over five years of experience with a deep interest in AI and machine learning. She excels in data-driven journalism, making complex topics both accessible and engaging for her audience. Her work is prominently featured on Techopedia, Business2Community, and Eurostat, where she provides creative technical writing. She holds a Bachelor of Arts Honours in English and a Master of Science in Strategic Management and Digital Marketing from the University of Malta. Maria's background includes journalism for, covering a range of topics from local events to international tech trends.