mlsanityData to 14 Oct 2026

FlexLink: Boosting your NVLink Bandwidth by 27% without accuracy concern

arXiv 2510.15882 ↗First posted 30 Aug 2025cs.AR cs.AI cs.DCcited by 2

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Abstract (arXiv)

As large language models (LLMs) continue to scale, multi-node deployment has become a necessity. Consequently, communication has become a critical performance bottleneck. Current intra-node communication libraries, like NCCL, typically make use of a single interconnect such as NVLink. This approach creates performance ceilings, especially on hardware like the H800 GPU where the primary interconnect's bandwidth can become a bottleneck, and leaves other hardware resources like PCIe and Remote Direct Memory Access (RDMA)-capable Network Interface Cards (NICs) largely idle during intensive workloads. We propose FlexLink, the first collective communication framework to the best of our knowledge designed to systematically address this by aggregating these heterogeneous links-NVLink, PCIe, and RDMA NICs-into a single, high-performance communication fabric. FlexLink employs an effective two-stage adaptive load balancing strategy that dynamically partitions communication traffic across all available links, ensuring that faster interconnects are not throttled by slower ones. On an 8-GPU H800 server, our design improves the bandwidth of collective operators such as AllReduce and AllGather by up to 26% and 27% over the NCCL baseline, respectively. This gain is achieved by offloading 2-22% of the total communication traffic to the previously underutilized PCIe and RDMA NICs. FlexLink provides these improvements as a lossless, drop-in replacement compatible with the NCCL API, ensuring easy adoption.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 13 references that are in the map. First posted on arXiv 30 Aug 2025.

Builds on

11 of 13 references.

References · 11

  1. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  2. 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,868
  3. 2025DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environmentscited by 286
  4. 2024FastDecode: High-Throughput GPU-Efficient LLM Serving using Heterogeneous Pipelinescited by 49
  5. 2025Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Expertscited by 49
  6. 2025MegaScale-MoE: Large-Scale Communication-Efficient Training of Mixture-of-Experts Models in Productioncited by 31
  7. 2025TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitivescited by 24
  8. 2024Pie: Pooling CPU Memory for LLM Inferencecited by 13
  9. 2024Flash Communication: Reducing Tensor Parallelization Bottleneck for Fast Large Language Model Inferencecited by 11
  10. 2024Optimizing ML Concurrent Computation and Communication with GPU DMA Enginescited by 2
  11. 2024Nezha: Breaking Multi-Rail Network Barriers for Distributed DNN Trainingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.