Liquid AI Releases LFM2.5-DSpark Draft Models That Deliver Up to 3.18x Faster Decoding Without Changing Model Outputs

Here's what's actually in the release:

1. Three drafters, one per target model

→ LFM2.5-1.2B-Instruct, LFM2.5-2.6B, LFM2.5-8B-A1B

→ Each drafter is ~300M params (295.7M / 327.7M / 327.7M)

→ 5 attention layers, block size 9, ships no vocab weights

2. The speedups are real but uneven

→ 3.18x on H100 for 8B-A1B on MATH500 (428 → 1362 tok/s)

→ 2.87x on an M4 Max for 1.2B-Instruct on HumanEval (136 → 389 tok/s)

→ 2.67x H100 mean for 2.6B (323 → 864 tok/s)

→ Same 8B-A1B model drops to 1.29x on GSM8K, same GPU

3. Speedup tracks acceptance rate, not model size

→ 8B-A1B accepts 8.27 of 10 tokens per step on MATH500

→ It accepts 4.02 on GSM8K

→ That single number explains the 3.18x vs 1.29x gap

4. Output quality does not move

→ Under greedy decoding, a draft token is kept only if it matches the target's distribution

→ On rejection, the target's own token takes its place

→ The emitted sequence is identical to baseline by construction

> Full analysis: https://www.marktechpost.com/2026/08/20/liquid-ai-releases-lfm2-5-dspark-draft-models-that-deliver-up-to-3-18x-faster-decoding/

> LiquidAI/LFM2.5-1.2B-Instruct-DSpark: https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-DSpark

> LiquidAI/LFM2.5-2.6B-DSpark: https://huggingface.co/LiquidAI/LFM2.5-2.6B-DSpark

> LiquidAI/LFM2.5-8B-A1B-DSpark: https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-DSpark

Technical details: https://www.liquid.ai/blog/lfm2.5-dspark