The secret to how large language models can become smarter while shrinking their massive size lies in MoE, which divides experts, and efficient hybrid structures.
Imagine this: You wake up in the morning and tell your AI assistant, “Summarize the key points of today’s meeting materials and give me the optimal route according to my afternoon schedule.” In the past, you needed an incredibly powerful supercomputer to process such complex requests, but now we can run surprisingly smart AI on our personal computers or smartphones. How did this become possible? Hidden at the core are magical technologies that efficiently improved the ‘brain structure’ of AI.
Why Is This Important?
In the past, the best-performing AI models could only be found within the servers of giant corporations. To increase performance, you had to grow the size of the model, which required enormous computing resources. [Source 12] However, as ‘open-weight’ models (models released so that anyone can download, run, and modify them on their own computers) have developed rapidly, the massive barrier between performance and efficiency is breaking down. [Source 12] This means that we can directly own more personalized, secure, and fast AI. It is now an era where not only corporations but also individual developers can create and utilize their own specialized AI. [Source 9]
Easy to Understand: MoE and Hybrid Structures
For an AI model to become smarter, it must learn and process a lot of information. However, using the entire brain for every question makes it too slow and consumes too much electricity. An innovative technology that appeared to solve this is the Mixture of Experts (MoE) model.
Let’s use an analogy. If the AI of the past was an ‘all-rounder who knows a little bit about everything,’ an MoE model is like ‘a team of experts gathered from various fields.’ [Source 8] The MoE architecture splits the massive neural network inside the model into several small ‘expert’ networks. [Source 7] When we throw a question at it, a ‘gating mechanism’ inside the model calls out only the necessary experts, saying, “The math expert should handle this problem!” or “Pass this to the writing expert!” [Source 7, Source 8] Consequently, we can obtain smart results by pulling out only the necessary knowledge without running the entire model, making it much more efficient. [Source 8]
Also, a hybrid structure that mixes technologies with different strengths has appeared. For example, a model called ‘Jamba’ interleaved a special module that enhances memory between the basic AI structure, Transformer (an AI structure that grasps relationships between words in sentences) layers. [Source 6] It’s similar to producing the best result by layering multiple filters in a photo editing app.
Current Situation: How Far Have We Come?
Related technologies are changing rapidly every day. In particular, the emergence of models that have changed the landscape of the open-source ecosystem is surprising. In August 2025, OpenAI released ‘GPT-OSS’, its first open-weight model since 2019. [Source 11] This model adopted an MoE design with 20B (20 billion) and 120B (120 billion) parameter sizes (a numerical value indicating the size of knowledge learned by the model), shocking the open-source industry. [Source 11]
We now live in an era where we can utilize various powerful models such as DeepSeek, Llama, Mistral, and Gemma in our own environments. [Source 13] However, as anyone can now modify the weights of a model (the core data learned by the model), establishing ‘sustainable safety protections’ to prevent malicious users from disabling or tampering with AI safety features has emerged as an important task. [Source 10]
What Will Happen in the Future?
In the future, the development will head in a direction where models become smaller yet much more powerful. Researchers are refining efficient structures like MoE and incorporating hybrid technologies to maximize performance. [Source 6] In particular, combined with techniques like ‘quantization’ (a technology that adjusts model precision to reduce size) or ‘LoRA’ (a technology that fine-tunes models at low cost), the day when we can run expert-level AI on general laptops will get even closer. [Source 9] AI will no longer be a distant presence but a familiar assistant moving at your fingertips.
AI’s Perspective (MindTickleBytes AI Reporter’s Perspective)
The advancement of open-weight models is not just a competition of numbers. It is a process where the powerful tool called artificial intelligence leaves a few servers and becomes a tool for the public. More efficient designs will allow more people to enjoy better AI, which will soon lead to the democratization of technology.
References
- A Dream of Spring for Open-Weight LLMs
- Sebastian Raschka’s 2026 LLM Architecture Gallery
- A Dream of Spring for Open-Weight LLMs (LinkedIn Post)
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[Applying Mixture of Experts in LLM Architectures NVIDIA Technical Blog](https://developer.nvidia.com/blog/applying-mixture-of-experts-in-llm-architectures/) - Mixture-of-Experts (MoE) Architectures: 2024–2025 Literature Review
- DBRX, Grok, Mixtral: The Rise of Mixture-of-Experts LLMs
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[MoE LLM Architecture: How It Works, Benefits And… SaM Solutions](https://sam-solutions.com/blog/moe-llm-architecture/) - Beyond the API: Engineering with Open-Weight LLMs - YouTube
- On Evaluating the Durability of Safeguards for Open-Weight LLMs
- Is Bigger Always Better? What GPT-OSS Reveals About MoE LLMs
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[Open-Weight LLMs in 2025: A Practical Guide Tequity](https://tequity.tech/insights/open-source-llm-guide) -
[Ultimate Guide: Run DeepSeek, Llama & LLMs Locally in 2025 e-verse](https://e-verse.com/learn/run-your-llm-locally-state-of-the-art-2025/)
- The entire model always processes all data
- It divides expert networks and activates only the necessary parts
- It unconditionally makes the model size larger
- It mixed Transformers with memory-augmented modules
- It uses only memory
- It repeats existing Transformer structures
- Pure dense structure
- Mixture of Experts (MoE) structure
- Simple linear model