DSpark
AssessTechniques
A proposed speculative decoding method for accelerating LLM inference.
Why it's here
Placed in Assess: 3 article(s) of evidence from 2 source(s), led by model releases, with 1 in the last 30 days. Confidence 48%.
Evidence (3)
- 7Hugging Face Blog·9/24/2026model_releaseLiquidAI releases DSpark draft model for LFM2.5-VL-3B
LiquidAI released an experimental DSpark drafter for its vision-language model LFM2.5-VL-3B, adding speculative decoding to speed up inference without changing output quality. The company reports decode speedups of up to 3.13x on-device and 2.66x on H100, with end-to-end gains up to 2.62x and 2.27x, and day-one support in llama.cpp, MLX-VLM, and SGLang.
- 7Hugging Face Blog·8/20/2026model_releaseLFM2.5 DSpark Draft Models Deliver Faster Inference
Hugging Face and Liquid AI released DSpark draft checkpoints for three LFM2.5 models, adding speculative decoding to improve inference speed without changing output quality. The company reports up to 3.18x higher GPU throughput, up to 2.87x on-device speedups, and a 57% average reduction in function-calling latency for LFM2.5-2.6B, with day-one support in llama.cpp and SGLang.
- 8Hacker News·6/27/2026researchDSpark: speculative decoding for faster LLM inference
DSpark is a research paper describing a speculative decoding approach aimed at reducing latency and improving throughput for large language model inference. It proposes a method that can accelerate generation while preserving output quality, making it relevant for serving and deployment efficiency.