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DGXArley: cluster deployment and transport reports

An operator-maintained K3s/Ansible repository for a four-Spark cluster. It contains deployment notes and reported transport/inference comparisons. Select the exact model profile and experiment before reusing a number.

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Source checked 2026-10-17 · Not independently reproduced by Run AI Here.

THE SOURCE IN CONTEXT

This document describes NCCL all-reduce profiling for a Spark serving cluster. It recommends inspecting small messages as well as the large-message bandwidth plateau. The author discusses roughly 185–190 versus 92–95 Gbit/s under different port-spanning conditions, but those are transport observations without an identified workload row here. They are not language-model decode speeds.

What is the report measuring?

This document describes NCCL all-reduce profiling for a Spark serving cluster. It recommends inspecting small messages as well as the large-message bandwidth plateau. The author discusses roughly 185–190 versus 92–95 Gbit/s under different port-spanning conditions, but those are transport observations without an identified workload row here. They are not language-model decode speeds.

Source: vroomfondel · pinned document

Why is a healthy headline link rate insufficient?

A standalone benchmark can inherit different interface, channel or RoCE settings from the serving container. Large messages can also hide the latency-bound region that tensor-parallel decode uses. Record the actual serving path and inspect its small-message behavior before attributing an inference slowdown to model kernels.

Source: vroomfondel · pinned document

How is the sweep defined?

The tool’s default range starts at 1 MB and ends at 1 GB, with a factor-two size step, five warmup and twenty timed iterations per size. The document asks operators to lower the floor to 8 KB for decode-oriented profiling. The ratio below 0.4 is an author-defined diagnostic flag, not an industry pass/fail threshold or a prediction of tokens per second.

Source: vroomfondel · pinned document

What belongs in a usable transport receipt?

Keep node count, per-node network device, MTU path, transport, process/container network settings, message size and both algorithm and bus bandwidth. Match those settings to the inference process before linking network results to generation performance. Maintenance and live-workload conflicts require the operator’s own planning; this site does not inspect or alter any cluster.

Source: vroomfondel · pinned document

What does the source specify?

These declarations belong to the cited document. A planning minimum, model limit and measured runtime allocation are different quantities.

Configuration facts from the pinned source
Decode-oriented message region8 KB–1 MB
Default warmup / timed iterations5 / 20
Author’s small-region warning thresholdratio below 0.4× overall peak

AI-assisted source reading and original editorial synthesis; no independent model execution.

Key reading notes

Source identity and full condition receipt

Source identity

Attribution and exact source identity
Original publishervroomfondel
Reference typePerformance report
Model IDMultiple model profiles; see the selected report
Default recipe artifactNot specified
Runtime topics["SGLang"]
Source revision5f1f3762797eac0ea1ad43e403fa979e3e2c3934
Source commit date — not experiment date2026-10-16T07:08:17Z
Source checked2026-10-17
Declared licenseLGPL-3.0

Reported conditions

Unknown fields remain unspecified; context limits and measured input lengths are different
systemNVIDIA DGX Spark / K3s cluster
node_count4
parallelismNot specified
kv_dtypeNot specified
context_limit_tokensNot specified
experiment_dateNot specified
model_revisionNot specified
weight_hashNot specified
runtime_execution_revisionNot specified
input_tokensNot specified
output_tokensNot specified
concurrencyNot specified
cache_stateNot specified

Limits

The complete original tables and raw evidence remain in the linked reports. Selected attributed figures retain their own conditions. Results from different models, weights, runtimes, workloads or cohorts must be read separately.

License scope

Repository declaration only; models, containers, datasets and per-file terms remain separate.

This index publishes original explanations, limited attributed factual selections and source pointers. It does not distribute model weights or source repositories. Third-party material retains its own terms.