In December 2023, Mistral AI quietly released Mixtral 8x7B — a model that matched GPT-3.5 Turbo on most benchmarks while being open source and dramatically more efficient to run. The secret? A decades-old technique called Mixture of Experts (MoE).
What Is Mixture of Experts?
Traditional transformer models are "dense" — every input token passes through every parameter in the network. A 70B parameter model uses all 70 billion parameters for every single token. This is wasteful.
MoE models take a different approach. Instead of one massive feed-forward network, they use multiple smaller "expert" networks. A gating mechanism routes each token to only 2 of the 8 experts. The result: Mixtral has 46.7B total parameters but only activates about 12.9B per token.
This means:
- Faster inference — fewer computations per token
- Lower memory bandwidth — only the active experts need to be loaded
- Better quality per FLOP — the model can be larger overall while staying fast
Mixtral's Impact
Mixtral 8x7B was not just an academic exercise. It immediately became one of the most practical open source models:
- Matched or beat GPT-3.5 Turbo on MMLU, HellaSwag, and other benchmarks
- Ran faster than LLaMA 2 70B despite similar quality
- Supported 32K context window
- Available under Apache 2.0 license
For the first time, you could self-host a model that genuinely competed with OpenAI's workhorse model.
The Bigger Picture: Efficiency Matters
The MoE revolution coincided with another major shift: the realization that making models bigger is not the only path forward. Several trends converged in late 2023 and early 2024:
Longer context windows — GPT-4 Turbo expanded to 128K tokens. Anthropic's Claude 2.1 reached 200K. These massive contexts require efficient architectures to be practical.
Quantization advances — GPTQ, AWQ, and GGUF formats made it possible to run large models in 4-bit precision with minimal quality loss. A 70B model that once needed 140GB of VRAM could now fit in 35GB.
Speculative decoding — generating multiple tokens at once by using a small "draft" model to predict what the large model would say, then verifying in parallel.
What This Means for Builders
If you are building AI products, the efficiency revolution means:
- Self-hosting is viable — you no longer need a data center to run competitive models
- Costs are dropping — inference is getting cheaper by the month
- Latency is improving — MoE and quantization mean faster responses
- Edge deployment is coming — models are getting small enough for on-device use
The gap between what you can do with an API call and what you can do on your own hardware is shrinking rapidly. For the Turkish AI ecosystem, this means more opportunities to build without being dependent on US-based API providers.
The MoE architecture is likely here to stay. Expect to see it in every major model family going forward.