Specter Achieves 1.50x Speedup in MoE Inference via Quantization-Enabled Speculative Offloading

·2026.10.21 19:09

Key point

The system uses a lightweight quantized draft model to predict expert usage, reducing PCIe I/O bottlenecks on commodity hardware.

Specter is an inference system that combines speculative decoding with expert offloading to address PCIe I/O bottlenecks in Mixture-of-Experts (MoE) models.
It achieves an average 1.50x speedup in time per output token (TPOT), reaching up to 1.88x over state-of-the-art systems under single-request inference.
A lightweight quantized draft model runs on the device to provide accurate multi-step lookahead over future expert usage, allowing the runtime to organize expert movement ahead of verification.
The approach makes memory-constrained, single-GPU MoE inference more practical on commodity hardware, with code available on GitHub.

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