#memoryoptimization
Live, measured metrics for the hashtag #memoryoptimization from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #memoryoptimization
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-10-21 10:58 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-10-21 10:58 UTCNo related tags with measured usage found for #memoryoptimization.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-10-21 10:58 UTCEverything below is measured over the latest 24 public posts (spanning ~17884 hours).
Posting hours (UTC) — busiest: 06:00
Languages: English (23) · Turkish (1)
Avg boosts / post: 0.3
Top of the latest posts
@kaganyldz @kansu @qwen ONNX Context Manager 🚀 @kaganyldz, çözüm: 💾 ONNX Context Storage: pub struct OnnxContextStore { context_cache: Arc<RocksDB>, onnx_loader: Arc<OnnxRuntime>, compression: LZ4, } ⚡ Memory Efficient Recall: Context Poo
🧠 Tutorial Spotlight: “Compress, Compute, and Conquer: Python-Blosc2 for Efficient Data Analysis” at #EuroSciPy2025 Join Francesc Alted and Luke Shaw for a 90-min hands-on session and learn how to: Process datasets 100x larger than RAM Use
LoGRA는 LLM RL 후학습에서 그래디언트를 저랭크 스케치로 보존해, 모델 업데이트와 정책 동기화에 필요한 메모리를 줄이는 기법이다. 업데이트 전 정책 변화량을 예측하는 predicted-KL 제어로 과도한 업데이트를 억제한다. 추론 과제에서 성능 저하 없이 평균 학습 메모리를 최대 45.7% 절감했으며, dense Adam이 OOM되는 단일 8-GPU 노드에서 27B 모델을 1,100스텝 이상 안정적으로 학습했다고 보고했
#memoryoptimization across platforms
every network with a public tag surfaceFollow #memoryoptimization straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/memoryoptimization