#modelefficiency

Live, measured metrics for the hashtag #modelefficiency from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.

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0
Uses / 7 days
Mastodon
0
Accounts / 7 days
Mastodon
12
Recent posts
Mastodon
~0/hr
Recent pace
Mastodon · last 12
0.1
Avg reactions / post
Mastodon · last 12

Day-by-day usage

measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-27 07:52 UTC
0
07-21
0
07-22
0
07-23
0
07-24
0
07-25
0
07-26
0
07-27

0 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 2026-07-27 07:52 UTC

No related tags with measured usage found for #modelefficiency.

Live pulse

measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-27 07:52 UTC

Everything below is measured over the latest 12 public posts (spanning ~9605 hours).

Posting hours (UTC) — busiest: 04:00

00:0012:0023:00

Languages: English (12)

Avg boosts / post: 0.3

Top of the latest posts

  • The AI world is buzzing over TurboQuant, Google Research’s new answer to the AI Memory Wall. This isn't just an incremental update; it’s a fundamental shift in how we think about hardware efficiency. By combining two new methods—PolarQuant

    BSR Tech News@[email protected]♥ 1↻ 12026-03-28 12:57 UTCView post →

  • Min Choi (@minchoi) Grok 4.5의 파라미터 규모가 1.5T이며, 2.8T 규모의 Kimi K3보다 작업당 비용이 약 3배 낮다고 주장한다. 대형 모델의 성능·비용 효율을 파라미터 수가 아닌 아키텍처·추론 효율 관점에서 비교하며, 더 큰 Grok 모델 확장 가능성을 전망한 의견이다. https://x.com/minchoi/status/2078239768459030811 #grok #kimi #llm #infe

    ainews@[email protected]♥ 0↻ 02026-07-18 04:52 UTCView post →

  • Observations on AI agent token consumption 스탠포드, 미시간, 딥마인드, 마이크로소프트 AI, MIT 연구진이 AI 에이전트의 토큰 소비를 대규모로 정량 분석한 논문을 발표했다. 에이전트 작업은 코드 채팅이나 단일 추론 작업 대비 약 1,000배 많은 토큰을 소비하며, 모델별 토큰 효율성 차이가 크고 토큰 사용량 예측이 매우 어렵다는 점을 밝혀냈다. 또한, 높은 토큰 비용이 반드시 더 나은 정

    ainews@[email protected]♥ 0↻ 02026-05-18 19:43 UTCView post →

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/modelefficiency