AI and Tech Summaries - AI Weekly Malaysia

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Date	Provider	Score	Summary
21 Aug 2026, 12:52 AM	Hugging Face Blog	7.0	Up to 3.2x Faster Inference with LFM2.5-DSpark LiquidAI released DSpark speculative decoding draft models (~300M params each) for three LFM2.5 models (1.2B, 2.6B, 8B-A1B), achieving up to 3.18x throughput on GPU and 2.87x on-device with no quality loss. The draft models use a DFlash-style parallel backbone plus a Markov-chain sequential head and a confidence-scheduled verifier, with day-one open-source support in llama.cpp and SGLang. Why: If you're shipping on-device or latency-sensitive LLM inference—especially agentic function-calling, where this cuts latency 57% on average for the 2.6B model—DSpark is a drop-in speedup you can test today via llama.cpp or SGLang without changing output quality. Evaluate whether swapping your current small-model serving path for LFM2.5 + DSpark beats your existing setup on your hardware. speculative-decoding inference-optimization on-device-ai llama-cpp sglang

Date

Provider

Score

Summary

21 Aug 2026, 12:52 AM

Hugging Face Blog

7.0

Up to 3.2x Faster Inference with LFM2.5-DSpark

LiquidAI released DSpark speculative decoding draft models (~300M params each) for three LFM2.5 models (1.2B, 2.6B, 8B-A1B), achieving up to 3.18x throughput on GPU and 2.87x on-device with no quality loss. The draft models use a DFlash-style parallel backbone plus a Markov-chain sequential head and a confidence-scheduled verifier, with day-one open-source support in llama.cpp and SGLang.

Why: If you're shipping on-device or latency-sensitive LLM inference—especially agentic function-calling, where this cuts latency 57% on average for the 2.6B model—DSpark is a drop-in speedup you can test today via llama.cpp or SGLang without changing output quality. Evaluate whether swapping your current small-model serving path for LFM2.5 + DSpark beats your existing setup on your hardware.