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

What changed

• Faster inference: up to 3.18 throughput improvement on a GPU and up to 2.87x on-device. - Toward on-device agentic inference: cuts function-calling latency by 57% on average for LFM2.5-2.6B - Day-one support for llama.cpp and SGLang: LFM-compatible DSpark integration is open-sourced upstream The decode phase in LLM inference is traditionally memory-bound. Most latency comes from streaming weights from DRAM into SRAM, not from intense computation.

Why it matters

A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.

How it compares

Related prior coverage to compare against:

• Speculative Decoding for 2x Faster Whisper Inference
• Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL
• LFM2.5-Encoders for Fast Long-Context Inference on CPU

• Speculative Decoding for 2x Faster Whisper Inference similarity 0.40
• Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL similarity 0.31
• LFM2.5-Encoders for Fast Long-Context Inference on CPU similarity 0.27