arXiv (All)
open access
PixelFlow: Token-Level Workload Management for Efficient Distributed DiT Serving
Online image generation with Diffusion Transformers (DiTs) must meet latency service-level objectives (SLOs) while using GPU resources efficiently. Existing systems improve GPU utilization by batching multiple requests for joint execution. However, request-level batching offers limited control over
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- PixelFlow: Token-Level Workload Management for Efficient Distributed DiT Serving#978388
- WavePP: High-Throughput Pipeline Parallel LLM Prefill under Prefix Reuse#1129857
- WavePP: High-Throughput Pipeline Parallel LLM Prefill under Prefix Reuse#1108699
- OpRAG: A Resource-Deterministic Runtime for GPU-Backed Multi-Stage RAG Workflows#441764
- ViBE: Co-Optimizing Workload Skew and Hardware Variability for MoE Serving#246529
Record · ID 1034520
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive