aiqbench analysis
Liquid AI's new DSpark drafters accelerate inference by up to 3.18x using speculative decoding without altering model outputs or accuracy. This optimization is particularly relevant for deploying smaller LFM2.5 models on local hardware like the M4 Max MacBook Pro while maintaining strict data privacy. The release expands practical deployment options for developers and enterprises requiring high-speed, on-device AI inference.
Liquid AI Releases LFM2.5-DSpark Draft Models That Deliver Up to 3.18x Faster Decoding Without Changing Model Outputs
Three ~300M drafters bring speculative decoding to LFM2.5, delivering up to 3.18x faster decoding with identical greedy output. The post Liquid AI Releases LFM2.5-DSpark Draft Models That Deliver Up to 3.18x Faster Decoding Without Changing Model Outputs appeared first on MarkTechPost.
Liquid AI has released DSpark draft model checkpoints for three models in its LFM2.5 family: LFM2.5-1.2B-Instruct, LFM2.5-2.6B, and LFM2.5-8B-A1B. Each drafter adds a speculative decoding path to an existing target model. A roughly 300M-parameter draft proposes a block of nine candidate tokens, and the target model verifies the whole block in a single forward pass. The trade is a small memory increase for a large decoding speedup: up to 3.18x on an H100 and up to 2.87x on an M4 Max MacBook Pro. Output does not change. Under greedy decoding, the emitted sequence is identical to the target model running alone,…
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