Skip to main content
Search

# Expert Parallelism

2 related articles

Subscribe to updates
  1. arXiv

    MoE serving re-splits expert replicas every decode step: Relief's integer relaxation nears the per-step optimum, and an on-die engine lifts effective utilization to 99.5%

    In expert-parallel MoE serving, each layer finishes only when its most loaded GPU does, and routing shifts at every decode step. Relief replaces the per-step linear program with an integer bidirectional pairwise relaxation that halves load gaps along replica edges and nears the per-step optimum in a few passes. On decode routing from three open MoE families, scored as effective utilization relative to that optimum, the deployed static scheme reaches 66% to 72%, the GPU kernel 93%, and the on-die engine 99.5%, with the largest recovery over static placement at 37.3 percentage points.

  2. NVIDIA prices agentic inference per byte: Vera Rubin NVL72 reaches up to 30x GB300 NVL72's throughput per megawatt

    NVIDIA reframes AI-factory memory from how much capacity a GPU has to how many useful tokens each byte produces, combining NVFP4 weight and KV-cache compression, wide expert parallelism for MoE weights, Dynamo KV-cache reuse, Vera CPU staging of context during tool calls, and CMX pod-level context storage. On SemiAnalysis AgentX, which replays recorded coding-agent sessions, Vera Rubin NVL72 delivers up to 30x the throughput per megawatt of GB300 NVL72 at 160 tokens per second per user, with both at 288 GB per GPU.

You’re all caught up