---
title: SpecPrefill on Unified Memory
emoji: "\U0001F4C4"
colorFrom: blue
colorTo: purple
sdk: static
pinned: false
license: mit
---

# SpecPrefill on Unified Memory: Cross-Architecture Sparse Prefill for Large Language Models on Apple Silicon

**Author:** David Green ([@Thump604](https://github.com/Thump604))

**Paper:** [specprefill-v2.pdf](specprefill-v2.pdf) | [specprefill.pdf](specprefill.pdf) | **Source:** [specprefill.tex](specprefill.tex)

**DOI:** [10.5281/zenodo.19120919](https://doi.org/10.5281/zenodo.19120919)

**Related:** [vllm-mlx PR #180](https://github.com/waybarrios/vllm-mlx/pull/180) (merged upstream)

## Abstract

Long-context prefill is the dominant latency bottleneck for local LLM inference: a 64K-token prompt on Qwen3.5-122B takes 7 minutes before the first token appears. SpecPrefill -- attention-based sparse prefill using a draft model -- reduces TTFT by 3.71-5.45x across 8K-128K tokens on Apple Silicon unified memory, cutting 128K prefill from 19.3 minutes to 3.5 minutes with a 1.4 GB draft model.