AgentServe: Algorithm-System Co-Design for Efficient Agentic AI Serving on a Consumer-Grade GPU
2026-03-12T08:52:33Z•a2fca645555d22a31fec85d45c3648d2c3800e4ad0f425e2cb4413541a5b08fb
AITERAMD InstinctCUDA Green ContextDGEMM emulationDMA orchestrationFP8GPU resource managementLLM servingOzaki‑IIRDMARaftRedFuserS‑HPLBTLA+ formalization','serverless anomaly detection','Hodge_decompagentic AIattention sparsityconsensus protocolscross‑domain latencydisaggregated inferencedmaplanehead‑parallelismhigh‑performance computinglatency stabilizationoperator fusionvLLM
What happened
This document bundles recent systems research (arXiv 12 Mar 2026) focused on high-performance AI infrastructure and deployment. Key contributions: AgentServe — a single‑GPU serving system that isolates long prefills from short decodes and uses CUDA Green Context slots to improve latency stability for multi-agent workloads (up to 2.8× TTFT, 2.7× TPOT vs. baselines); S‑HPLB — sparsity‑aware head‑parallel load balancing for attention that assigns head‑adaptive sparsity budgets and cuts average attention latency ≈2.88×; CD‑Raft — Raft protocol optimizations for cross‑domain consensus with formal T
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_dc
- Record identifier
- a2fca645555d22a31fec85d45c3648d2c3800e4ad0f425e2cb4413541a5b08fb
- Enrichment time
- 2026-03-12T08:52:33Z
- AI-assisted enrichment
- Yes
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