← Thursday, October 15, 2026Models & trainingDeep dive
Meta scales GEM’s compute engine and model across its ads ecosystem
Meta describes scaling its GEM recommendation foundation model, compute engine and hardware, building on earlier performance gains. The work highlights the compute and deployment challenges of applying large models to recommendation systems.
- What
- Meta's 2026 GEM generation replaces isolated per-domain ads models with one foundation model spanning its ads platforms.
- How
- Kunlun supports larger models and longer context; LoopFM transfers GEM representations and sequential memory to downstream models without added compute or latency.
- Why it matters
- This separates large-scale learning from real-time serving, addressing the cost and latency of evaluating a large model for every ad candidate.
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