{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your inference service slows down when several customers send requests together.",
    "change": "Replay a recorded request trace with only the proposed serving policy changed.",
    "baseline": "Your current scheduler on the same model and hardware.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Requests meeting your latency target, throughput, failures, and cost per successful request.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.28620",
  "title": "Weight Placement Is Not Enough: Synchronization in Multi-Node CPU Decoding of Ternary LLMs",
  "summary": "The method replaces weight-placement-only NUMA optimization in multi-node CPU decoding with a redesign that cuts inter-thread barrier waits, using a Megatron-style column split without cross-node reductions on top of ik_llama.cpp kernels. Its cost is implementation complexity in a custom decode engine and it applies only to ternary-quantized models; the reader would also need dual-socket, multi-NUMA-node Xeon-class servers. It can fail outside its tested regime: gains shrink when decoding is not DRAM-bound, when models fit in L3 cache, and on single-socket or GPU-based deployments where the synchronization pattern does not apply.",
  "rationale": "The technique requires multi-socket NUMA CPU servers running ternary models, which is entirely outside the reader's single 24 GB GPU or API-based infrastructure.",
  "technique": "NUMA-aware synchronization reduction for multi-socket CPU decoding",
  "family": "serving_e_batching",
  "recommendation": "nao_aplica",
  "published": "2026-10-20",
  "as_of": "2026-10-21",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.28620",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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      "dimension": "compatibility",
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