How DeepSeek’s Architecture Actually Works

DeepSeek’s Model Architecture, Simply Explained

AI, But Simple Issue #117

In 2026, it's common to hear stories of autonomous coding agents with Claude Fable 5 or GPT-5.6 burning through hundreds of dollars in API credits in under an hour.

Frontier capabilities have been scaling impressive heights, but the compute costs of this rise have made running these models at scale economically brutal.

Those costs represent the fork in the road for most users between their wallet and their needs.

It is also precisely why DeepSeek’s architecture captured the industry's attention.

By reapproaching traditional LLMs on the compute weight and storage aspects, DeepSeek became the example for how smarter architectural choices can deliver frontier performance at a fraction of the cost.

Today, we’ll provide a visual breakdown of the three architectural pillars that make the cost-cutting possible:

1. DeepSeek Mixture-of-Experts (MoE): Why fine-grained expert segmentation and shared expert routing yield higher specialization with lower active parameter counts.

2. Compressed Attention Mechanisms: How Multi-Head Latent Attention (MLA) and its evolution into dual-path hybrid attention beat standard quadratic-scaling mechanisms across 1M token context windows.

3. Group Relative Policy Optimization (GRPO): Adjusting the RLHF dramatically cuts training memory overhead.