SpecPrefetch: Parameter-Efficient Expert Prefetching for Sparse MoE Foundation Models
Quick Answer
SpecPrefetch introduces a parameter-efficient prefetching framework for Sparse MoE models, improving expert recall in 9 out of 10 benchmarks while reducing loading latency.
Quick Take
On Snapdragon 8 Elite, it enhances decoding throughput by up to 20% compared to optimized offloading runtimes, making it ideal for storage-constrained deployments.
Key Points
- SpecPrefetch uses a lightweight adapter for asynchronous expert candidate transfer.
- It maintains pretrained routing semantics, minimizing prediction errors' impact on model outputs.
- Achieves best average expert recall across Qwen3-VL-30B-A3B and DeepSeek-VL2-Tiny.
- Reduces expert-loading latency without increasing trainable parameters.
- Demonstrates practical benefits for MoE deployment on resource-constrained devices.
DeepSignal Analysis
What happened
SpecPrefetch is a new framework designed to enhance the efficiency of Sparse Mixture-of-Experts (MoE) models by improving expert recall and reducing loading latency. It separates the prediction of expert candidates from the execution routing, which allows for faster inference without altering the pretrained routing semantics. The framework has shown significant improvements in performance on various benchmarks and devices.
Key evidence
- SpecPrefetch achieves the best average expert recall in 9 out of 10 model-benchmark settings, indicating its effectiveness in optimizing expert selection.
- On a Snapdragon 8 Elite device, SpecPrefetch improves decoding throughput by up to 20% compared to optimized offloading runtimes, showcasing its practical benefits.
- The framework uses a lightweight adapter for asynchronous transfer of expert candidates, which reduces loading latency without changing the existing routing mechanisms.
Why it matters
The development of SpecPrefetch addresses a critical bottleneck in Sparse MoE models, where expert offloading can lead to delays due to routing-dependent transfers. By improving expert recall and reducing latency, this framework could enhance the deployment of MoE models in environments with limited memory, such as mobile devices. This is particularly relevant as the demand for efficient AI models grows in various applications.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Sparse Mixture-of-Experts (MoE) models expand foundation model capacity through conditional expert activation, but their full expert pools remain difficult to deploy under limited accelerator memory. Although expert offloading alleviates memory pressure by moving inactive experts to host memory or storage, it introduces a routing-dependent transfer bottleneck: required experts are known only after native top-\(K\) routing, which serializes routing, expert loading, and expert execution during inference. To address this bottleneck, we propose SpecPrefetch, a parameter-efficient prefetching framework for offloaded MoE inference. SpecPrefetch uses a shared lightweight adapter to predict next-layer expert candidates only for asynchronous transfer, while the frozen native router still determines the final executed experts. By separating transfer prediction from execution routing, SpecPrefetch reduces exposed expert-loading latency without changing pretrained routing semantics, so prediction errors affect transfer efficiency rather than model outputs. In addition, a window-aware scheduler prioritizes feasible transfers under cache and bandwidth constraints. Across Qwen3-VL-30B-A3B and DeepSeek-VL2-Tiny, SpecPrefetch achieves the best average expert recall in 9 out of 10 model-benchmark settings with substantially fewer trainable parameters than learned predictor baselines. On a Snapdragon 8 Elite device, SpecPrefetch further improves decoding throughput by up to \(20\%\) over a compute-optimized offloading runtime, demonstrating practical benefits for storage-constrained MoE deployment. The code and model weights are available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.24787 [cs.AI] |
| (or arXiv:2607.24787v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.24787 arXiv-issued DOI via DataCite |
Submission history
From: Runqi Meng [view email]
[v1]
Wed, 24 Jun 2026 04:53:00 UTC (781 KB)
— Originally published at arxiv.org
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