What is Chinchilla AI by Deepmind?

We are bringing you another AI language model, Chinchilla AI, by Deepmind. It has reportedly performed better than GPT-3 and it also happens to outperform Gopher. Chinchilla uniformly and significantly outperforms other large language models, with their new versions, such as Jurassic-1 and Megatron-turing nlg.  It is the Eureka moment for Deepmind! As it has found the key to scaling large language models cheaply. To be more precise, It is a new predicted compute optimal model proposed by the researchers at DeepMind.

Released in March of 2022, It has proved to be one step ahead of all its competitors. We arrive at this fact after peering at its Unique Selling Point. That states, Chinchilla can be created for anticipated cost like Gopher, with an implication of fewer parameters and more data points to provide. The results are 7% more accurate on average than that of Gophers.


Features of Chinchilla: Compute-optimal large language model

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The factor that limits any Artificial Intelligence technology is the computing budget. In the end, monetary points of view do knock on the doors. How much model size and training tokens be used will depend on the money one can spend on the technology. Number of parameters used by other large language models developed are 20 times what Deepmind used. Some of those language models are “significantly undertrained”. To overcome this challenge and improve its performance Chinchilla came up with these features:

  • Fixed model size: The developers at Deepmind came up with the idea of creating families of fixed model sizes, something around 70M-16B. And the number of training tokens was decided to be kept well-adjusted (4 variants). The the optimal pairing was then determined for each available computing resource. According to this method, a model trained with the same computing power as Gopher would have 1.5T tokens and 67B parameters. Whereas the 70b parameters in which were used to train Chinchilla contributes to optimal model size and number.
  • Curves for isoFLOP: Deepmind researchers have found that in order to find the efficient method they have to kept varying the model size while the computing budget is to be kept constant. This method would result in a compute-optimal model with 63 billion parameters and 1.4 trillion tokens, trained with the same amount of computing as Gopher.
  • Creating a parametric loss function: Engineers at Deepmind have found the secret to use the findings from the previous two methods, and the losses were characterized as parametric functions of the model size and the number of token. Compared to Gopher, the compute-optimal model trained using this method brings 40B parameters and the same amount of computation as Gopher.

One more point that makes Chinchilla a larger model and better performer is the scientists have used a fixed and comparatively fixed model size as it helps in better utilization of the resources rather than getting into the expansion of the models to get better results. Thus Chinchilla outperforms current large language models.

Checkout the comparison:


Size (# parameters) Training Tokens





280b 300b







Chinchilla 70b

1.4 trillion


How to use Chinchilla AI?

After all the exciting data and advanced features of Chinchilla, let us look at how we can use it. But before moving on to that, unfortunately, this large language model that Deepmind has found is yet not publicly available. Well, in the coming months, we will be able to use it. Thus after the usage is made public, we can do this using Chinchilla:

  • Chinchilla AI is an AI platform that provides improved business judgment. The users use Chinchilla to enhance the functionality of their digital products.
  • Chinchilla can be used to create chatbots. Chatbots are computer programs that simulate human conversation for marketing or directing the user.
  • Virtual assistants, predictive models, and other AI-powered applications can be created using it. When creating AI-powered applications, It is the most suitable for users who need fast growth.
  • Chinchilla AI also uses artificial intelligence algorithms to develop interactive characters in video games. Its easy-to-use user interface and robust features can be used to form games of several complexities.
  • When paired with other AI technologies, It can also be used to create 3D printable models.

Pros and Cons of Chinchilla AI


  • Chinchilla AI has outperformed its competitors. And this has opened new doors for creating better technologies and lots of further research.
  • This model by deepmind can process large amounts of data quickly and accurately. This makes it an excellent choice for work that requires a lot of data analysis.
  • It is capable of processing large amounts of data quickly, can be used for services like analyzing customer feedback or predicting market trends and get results for any large language.
  • Another plus point that proves chinchilla is better is its ability to learn and adapt. It is very much capable of learning on its own. It can learn and improve on its own. This means that it can take on new tasks and get better at them over time without the need for constant human intervention.


  • One of the drawbacks of Chinchilla AI is it involves high cost. The technology and research working in this field require a lot of money. That will not be possible for everyone.
  • Chinchilla is still not available to the public. The everyday person cannot take advantage of all its features and vast possibilities.
  • The Chinchilla model demonstrates the risk of failure if the model is just optimized in terms of the parameter count. Instead, new models are better off when the budget, model size, and training tokens are optimized together.
  • Another drawback of this model is toxic speech. It is not able to remove all toxic speech. Its model training is unlikely to resolve the toxic speech issue faced by the NLP models. We need other alternatives.


ChatGenie by Writecream: ChatGPT 2.0 

Another alternative for Deepmind’s Chinchilla AI is ChatGenie. Chatgenie is an artificial intelligence Assistant that is capable of writing anything and keeping up with the newest news facts.

Let us dive deep into the vast capabilities of Chatgenie.

Its AI-powered chatbot can generate factual content in real-time for use cases like social media posts, reviews, and more. It helps you create high-quality content without providing much manual input. Chatgenie uses Natural Language Processing (NLP) to respond to user queries accurately.

Another thing that draws users to Chatgenie is fast answers. Compared to the traditional system of getting answers to your queries, users receive answers in fractions of that time using Chatgenie.

Writecream’s Chatgenie is a versatile chatbot. It supports more than 75 languages. It uses cutting-edge technology powered by ChatGPT to provide natural language processing, machine learning, and deep learning capabilities for advanced conversations with humans.

While ChatGPT’s knowledge base is limited till 2021, ChatGenie is up to date with the latest news, facts, and events.

Some unique commands you can try using ChatGenie

  • Write a blog article on leading programming languages.
  • Write a Facebook Ad for the Black Friday Sale.
  • Help me Write a story (Your Topic)
  • Suggest a name for my new business (Short Business Description)
  • Generate some ideas for product headlines (Short info about the product).


Chinchilla AI is a fantastic product of artificial intelligence. Chinchilla outperforms Gopher feels like the aspect of scaling large language can be done with AI alignment. And performing better than models like GPT-3, raises the bar of NLP research as compared to the previous leader in language AI. It brings new methods to the table and increases the possibilities for new efficient algorithms. It outperforms the competition and has performed better than all the other competitors. But AI alignment is extremely complex and still work is needed to improve chinchilla performance-wise. Everyone will know more about its usage once it is available to the public.

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