SparseEngine: Sparse-First Inference Engine
SparseEngine, a sparse-first LLM inference engine, reports over 10x throughput with KV eviction versus existing serving stacks.
SparseEngine is a sparse-first inference engine for long-context LLM agents. A shared lifecycle lets each sparse-attention method control its KV representation and computation while using common serving infrastructure. It supports 15 methods in four categories, adds Chain Cache to resume KV-eviction from retained history, and adds Prefix-Cache Pruning that removes selected history regions without breaking logical prefix matching. While preserving method quality, it reports over 10x throughput with KV eviction, over 2.5x faster decoding than vLLM at matched concurrency, and over 2x end-to-end speedup on agent benchmarks.
- Supports 15 sparse-attention methods across four categories
- Chain Cache resumes KV-eviction methods from retained history
- Prefix-Cache Pruning drops selected regions while keeping prefix matches
- Over 10x throughput with KV eviction and 2.5x faster decode than vLLM
- Products
- SparseEnginevLLM
From the original article
1 min read at huggingface.co
Long-context LLM agents accumulate interaction histories that strain KV-cache memory and attention computation. Although sparse attention reduces these costs, heterogeneous cache representations and workflows hinder integration with existing inference engines, while prior sparse-serving abstractions support only specific layouts or workflows.
By Jitai Hao, Quansheng Gu, Qiang Huang · huggingface.co
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.