Building Smarter AI Systems (Mixture Of Agents)

From Mixture of Experts to Mixture of Agents: Building Smarter AI Systems

*How Cerebras is revolutionizing AI inference with ultra-fast hardware and innovative agent architectures*

The evolution of large language models has reached an inflection point. As models grow larger and more capable, we face fundamental challenges in scaling them efficiently. At a recent Cerebras workshop, researchers demonstrated how to move beyond traditional monolithic models toward a new paradigm: Mixture of Agents (MoA).

The Evolution of Large Language Models

The journey from GPT-3 to today's frontier models tells a story of relentless scaling. GPT-3 started at 175 billion parameters, Llama 3 reached 400 billion, and DeepSeek-V3 now boasts 600 billion parameters. But simply adding more parameters isn't sustainable without architectural innovations.

Three key factors have driven model improvements:

1. **Model Size**: Larger parameter counts generally lead to better performance

2. **Data Quality**: Curated, high-quality training datasets significantly impact model capabilities

3. **Architecture Innovations**: New designs like Mixture of Experts enable efficient scaling

Understanding Mixture of Experts

Mixture of Experts (MoE) represents a fundamental shift in how we design neural networks. Traditional transformer architectures process all information through the same feed-forward networks, creating bottlenecks when handling diverse tasks across different domains and languages.

MoE solves this by replacing monolithic feed-forward networks with specialized "experts" - separate networks that excel at specific tasks. A router network intelligently directs tokens to the most appropriate expert, whether that's a math specialist, biology teacher, or language translator.

The key advantage? You can dramatically increase model parameters without proportionally increasing inference time, since only relevant experts activate for each token. This allows models to become more capable while maintaining efficiency.

The Challenge of Inference Time

Current reasoning models like GPT-o3 showcase impressive capabilities but suffer from significant speed limitations. In the workshop demonstration, GPT-4o took 45 seconds to produce an incorrect answer to an AMC math problem, while GPT-o3 required 293 seconds to get it right.

For real-world applications, waiting nearly five minutes for a response is impractical. Users expect sub-second response times, not something that feels like "three business days."

 Enter Mixture of Agents

Mixture of Agents takes inspiration from MoE but operates at the inference level rather than during pre-training. Instead of training one massive model with expert components, MoA combines multiple pre-trained models through intelligent orchestration.

The process works like this:

1. **Input Distribution**: A user query is sent to multiple specialized agents, each with custom system prompts

2. **Parallel Processing**: Each agent processes the query independently, leveraging their specific strengths

3. **Response Aggregation**: A final model combines all individual responses into a comprehensive answer

4. **Iterative Refinement**: Multiple layers can be used for complex problems requiring sequential reasoning

Real-World Performance

The workshop featured a compelling case study from NinjaTech.ai, which solved the same AMC math problem in just 7.4 seconds using a mixture of agents approach. Their system uses:

- A **Planning Agent** that generates multiple solution proposals

- A **Critique Agent** that evaluates feasibility

- A **Summarization Agent** that combines the best answers

This process involved over 500,000 tokens and 32 LLM calls (both parallel and sequential) but delivered accurate results in a fraction of the time required by reasoning models.

 The Cerebras Advantage

Cerebras hardware provides the foundation that makes MoA practical. While traditional GPUs like the H100 have around 17,000 cores with memory largely stored off-chip, Cerebras chips contain 900,000 cores with dedicated on-chip memory for each core.

This architecture eliminates memory transfer bottlenecks that plague traditional setups. The only data transferred between chips are activations - a minimal amount that can be sent over a single Ethernet connection. This design enables Cerebras to achieve speeds 15.5 times faster than the fastest GPU-based inference providers for models like Llama 3.1 70B.

Building Your Own Mixture of Agents

The workshop provided hands-on experience with configuring MoA systems. Participants learned to optimize:

- **Model Selection**: Choosing the right base models for different agents

- **System Prompts**: Crafting specialized instructions for each agent

- **Layer Configuration**: Determining the number of processing layers and iterations

- **Temperature Settings**: Balancing creativity and consistency

The key insight is that MoA isn't plug-and-play - it requires careful engineering and optimization for each use case. However, when properly configured, it can outperform even frontier models on specific tasks.

 Practical Applications

MoA systems excel in scenarios requiring:

- **Complex Problem-Solving**: Breaking down multi-step problems into specialized components

- **Domain Expertise**: Combining knowledge from different fields

- **Quality Assurance**: Using critique agents to validate and improve outputs

- **Speed Requirements**: Achieving high-quality results faster than reasoning models

 The Future of AI Architecture

As we approach potential data walls in pre-training, inference-time compute becomes increasingly important. MoA represents a promising direction for achieving greater intelligence without requiring exponentially larger models.

The combination of specialized agents, ultra-fast inference hardware, and intelligent orchestration points toward a future where AI systems can be both incredibly capable and practically usable in real-time applications.

 Getting Started

For developers interested in exploring MoA systems, Cerebras provides API access and workshop materials. The key is to start with well-defined use cases, carefully engineer your agent prompts, and iteratively optimize the system configuration.

As the field continues to evolve, MoA offers a compelling alternative to the current paradigm of ever-larger monolithic models, promising both improved performance and practical deployment advantages.

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*This post is based on insights from a Cerebras workshop on Mixture of Agents, featuring research from their team of AI scientists and engineers working on next-generation model architectures.*