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distributed inference

topic3 events
papersTODAY 04:00 UTC

Paper Proposes Affinity-Aware Sharding to Improve Delayed Tensor Parallelism

A new arXiv preprint examines Delayed Tensor Parallelism (DTP), a scheme that avoids the blocking all-reduce step in tensor-parallel Transformer inference by letting each device immediately add its partial output to the residual stream and broadcast it. The authors introduce affinity-aware sharding as a way to organize this communication and computation across devices. The work targets more efficient distributed inference for large Transformer models.

distributed-training/inference/transformers/arxiv
papersTODAY 04:00 UTC

MANE: Multi-Path Adaptive Network for Edge Offloading of Deep Neural Networks

Researchers propose MANE, a multi-path adaptive network designed to improve split computing, where a small head model runs on a device and a larger tail model runs on an edge server. The approach targets efficient distributed inference by adapting how computation is divided between the device and the edge. It is described in a new arXiv preprint (2609.14660v1) listed under cross-submissions.

edge-computing/inference/distributed-systems/research
papersSEP 10 04:00 UTC

X-CoSD: Cross-Vocabulary Collaborative Speculative Decoding for Efficient LLM Inference

Researchers introduce X-CoSD, a distributed inference framework in which a small on-device model drafts tokens and a larger server-side LLM verifies them. The method tackles the mismatch between the two models' vocabularies while cutting the communication cost between device and server. The work aims to make collaborative speculative decoding practical for latency-sensitive edge deployments.

inference/edge-ai/efficiency/distributed-systems