- Updated: August 26, 2026
- 2 min read
SAEM: Stage‑Aware Expert Management for Memory‑Efficient MoE Inference in Chain‑of‑Thought Reasoning
SAEM: Stage‑Aware Expert Management for Memory‑Efficient MoE Inference in Chain‑of‑Thought Reasoning
Abstract
Chain‑of‑thought (CoT) prompting dramatically improves large‑language‑model (LLM) reasoning by breaking complex problems into intermediate steps. However, the sequential nature of CoT increases decoding latency and memory consumption. Mixture‑of‑Experts (MoE) models alleviate capacity limits through sparse expert activation, yet their full expert weights often exceed GPU memory, forcing costly GPU‑CPU transfers.
The newly proposed SAEM runtime detects reasoning‑stage boundaries within CoT traces and exploits the inherent coherence of expert activation across each stage. By aligning token repacking with stage‑level expert patterns, SAEM enables stage‑aware caching, expert‑aligned token grouping, and in‑situ CPU execution, dramatically reducing data movement and kernel fragmentation.
Key Contributions
- Stage‑aware caching that keeps frequently used experts resident on GPU for the duration of a reasoning stage.
- Expert‑aligned token repacking to improve memory locality and kernel efficiency.
- In‑situ CPU execution for low‑frequency experts, eliminating unnecessary GPU‑CPU transfers.
Performance Highlights
On benchmark mathematical and scientific reasoning workloads, SAEM delivers:
- Average 1.33× throughput improvement over the strongest state‑of‑the‑art caching and offloading baselines under constrained GPU memory.
- Up to 1.54× improvement when calibration data matches the workload.
These results demonstrate that stage‑aware, locality‑driven MoE inference can significantly accelerate CoT reasoning while staying within memory limits.
Why It Matters for Ubos Tech
Integrating SAEM into our LLM services enables faster, more cost‑effective reasoning capabilities for our customers. The reduced memory footprint allows us to serve larger MoE models on existing GPU infrastructure, delivering higher‑quality answers without additional hardware investment.
Read the full paper on arXiv for detailed methodology and experimental results.
Stay tuned for upcoming tutorials and implementation guides on the Ubos Tech blog.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.