GPU AI workloads with a Ruby on Rails monolith
CPU and GPU work overlap for the duration of the document. Measured against the same pipeline executed synchronously, this yields approximately 80% higher throughput. Streaming results to the browser Fields detection runs in a Sidekiq worker on the GPU instance, and to stream results back to the user’s browser over Server-Sent Events, we use Redis pub/sub to carry messages from the worker process to the web process.
- ▪CPU and GPU work overlap for the duration of the document.
- ▪Measured against the same pipeline executed synchronously, this yields approximately 80% higher throughput.
- ▪Streaming results to the browser Fields detection runs in a Sidekiq worker on the GPU instance, and to stream results back to the user’s browser over Server-Sent Events, we use Redis pub/sub to carry messages from the worker process to the
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,428 of its stories.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DocuSeal |
| Canonical URL | https://www.docuseal.com/blog/gpu-ai-workloads-with-a-ruby-on-rails-monolith |
| Publication time | Tue, 11 Aug 2026 12:08:58 +0000 |
| Retrieval time | 2026-08-11T12:10:47.911Z |
| Last seen | 2026-08-11T12:10:47.911Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 1lmdgfjg5vqP · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
CPU and GPU work overlap for the duration of the document. Measured against the same pipeline executed synchronously, this yields approximately 80% higher throughput. Streaming results to the browser Fields detection runs in a Sidekiq worker on the GPU instance, and to stream results back to the user’s browser over Server-Sent Events, we use Redis pub/sub to carry messages from the worker process to the web process. Using Rails Action Cable with WebSockets would be a viable option as well, but we chose to stick with a plain Rails Live SSE controller. The worker publishes each page’s fields as soon as that page completes, allowing us to show the user a live progress indicator of the number of pages processed.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DocuSeal.
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