Open source LLaMA 4 released, a 288B parameter giant, traditional RAG may be out of work!
Meta AI's open source large model LLaMA 4 challenges RAG and opens a new era of AI.
Core content:
1. LLaMA 4's ultra-long context processing capability subverts the traditional RAG model
2. Parameter comparison of three models Llama 4 Scout, Maverick, and Behemoth
3. Open source LLaMA 4 leads the new trend of AI, and long-term memory of future models becomes possible
When LLaMA 4's super-long context came out, RAG trembled.
The smallest model, Llama 4 Scout, comes with 10M tokens (10 million tokens).
What is the concept of 10M token?
About 15MB of text can hold 100 books or 1 million lines of code. LLaMA 4 can "read" super long content in one go without having to feed the data over and over again.
We know that traditional RAG (Retrieval-Augmented Generation) feeds the model by retrieving external knowledge bases to solve the problem of short context.
In the past, we had to analyze 100 books and 1 million lines of code. With RAG, we first have to retrieve relevant fragments and then feed them to the model to generate answers.
Now, the emergence of LLaMA 4 seems to prove that this demand is still in the wild age of AI. Just throw all the data to LLaMA 4 Scout, and it will translate and infer by itself and give you the answer.
"Full input + direct reasoning" can be said to directly beat RAG's "retrieval + generation" mode. Ordinary users no longer need to build complex retrieval pipelines (vector databases, embedded models), and the cost of use or development has dropped sharply.
Here is an introduction to LLaMA.
Meta AI is known as the "Android of AI" , and LLaMA is a series of open source large language models (abbreviation of Large Language Model Meta AI) developed by Meta AI, which debuted in 2023. It is designed for research and efficiency, and is known for its powerful natural language processing capabilities at a smaller parameter scale. Compared with commercial closed-source models (such as the GPT series), LLaMA focuses on open source and lightweight.
On April 5, 2025, Meta AI officially released the fourth-generation family of this open source large language model, claiming to be "the most advanced multimodal model of its kind."
There are three models of LLaMA 4.
Llama 4 Scout : 17B active parameters, 16 expert modules, multimodal (text + image), 10M tokens in context window. Can run on a single H100 GPU. Better than LLaMA 3.1 405B, Gemma 3 and Gemini, known as "the most powerful multimodal model on a single card".
Llama 4 Maverick : 17B active parameters, 128 expert modules, performance is said to exceed GPT-4 and is comparable to DeepSeek v3, the "open source ceiling".
Llama 4 Behemoth : Directly comes to 2880B parameters, 16 expert modules, still in training, aiming to challenge GPT-4o.
In addition to the large parameters, the open source Llama 4 is also fully multimodal, with native fusion of text and images. It is currently available on opentrouer and togetherai can also be used for free.
Although it cannot be run directly on consumer-grade graphics cards at present, and the performance of Llama 4 may be just so-so, Llama 4 is clearly sending a signal: the era of large models and large windows has arrived , and the RAG method will soon be iterated. In the future, everyone will have their own exclusive model, and long-term model memory will also become possible. New knowledge and new content may be directly internalized into the model by adding it directly.
