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Morning Edition · Thursday, September 10, 2026Published at 2:24 AM EDT · New York
Two Speculative-Decoding Papers Attack the Fragility That Limits Inference Speedups
One splits drafting between an on-device small model and a server verifier across different vocabularies, the other pretrains drafters without a fixed target model to stop acceptance rates collapsing under workload shift.
Speculative decoding, in which a small model drafts tokens that a large model verifies in parallel, is the standard way to cut latency for reasoning models. Its weakness is well known to anyone running it in production: drafters are trained…
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Part of a tracked trend
The Inference-Cost Efficiency Race
Techniques that cut tokens generated and KV-cache memory per query will keep compressing the marginal cost of serving reasoning models, making inference efficiency a recurring competitive axis alongside raw capability.
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