Multi-Modal Frontiers & Vector Architecture
Mixture of Experts (MoE)
Mixture of experts activates only a subset of parameters per token, delivering large-model quality at lower compute. The trade is memory: all experts must be loaded even when few are active. MoE rewards careful routing and punishes imbalance and expert collapse.
要点
- Understand that MoE trades compute for memory; plan VRAM around total parameters.
- Monitor expert utilization to catch collapse or severe imbalance.
- Tune routing and capacity factors against both quality and throughput.
- Quantize experts and attention differently based on sensitivity.
- Benchmark on your real prompts; MoE gains vary by task and batch size.
- Keep a dense baseline to compare quality and cost honestly.
- Watch communication overhead in multi-GPU setups with expert parallelism.
- Profile memory and KV-cache usage before committing to deployment.
よくある質問
Is MoE always faster than dense?
On throughput yes often, but memory and routing overhead can offset gains.
Does MoE reduce quality?
Well-trained MoE matches dense quality; poor routing degrades it.
Can I run MoE on one GPU?
Small ones yes; large ones usually need multi-GPU memory.
What is expert collapse?
When routing sends most tokens to a few experts, wasting capacity.
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