{"paper":{"title":"Lever: Speculative LLM Inference on Smartphones","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Lever reduces smartphone LLM inference latency by 2.93x over flash baselines through optimized speculative decoding.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fengzu Li, Ju Ren, Tuowei Wang, Wei Gao, Yanfan Sun","submitted_at":"2026-05-16T03:43:10Z","abstract_excerpt":"Large language models (LLMs) are increasingly needed for interactive mobile applications, but high-quality models exceed the limited DRAM available on smartphones. Flash storage can hold larger models, yet flash-backed inference is slow because autoregressive decoding repeatedly invokes the target model and incurs costly I/O. We observe that speculative decoding is a natural fit for this setting: a small draft model can remain in DRAM, while a larger flash-resident target model verifies multiple candidate tokens per invocation. However, existing methods assume server-class accelerators and fai"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Lever reduces inference latency by an average of 2.93x over baseline flash-offloaded inference and 1.50x over conventional speculative decoding, narrowing the latency gap between flash-backed and memory-resident LLM inference.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that jointly optimizing token-tree construction with an I/O- and compute-aware gain-cost objective, early-exit pruning, and CPU-NPU mapping will deliver the claimed speedups under real smartphone I/O latency and parallelism constraints (stated in the abstract description of the three stages).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Lever optimizes the drafting, 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