#applemlx

Live, measured metrics for the hashtag #applemlx 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
3
Recent posts
Mastodon
~0/hr
Recent pace
Mastodon · last 3
0
Avg reactions / post
Mastodon · last 3

Day-by-day usage

measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-27 11:25 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 11:25 UTC

No related tags with measured usage found for #applemlx.

Live pulse

measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-27 11:25 UTC

Everything below is measured over the latest 3 public posts (spanning ~2811 hours).

Posting hours (UTC)

00:0012:0023:00

Languages: English (3)

Avg boosts / post: 0

Top of the latest posts

  • Apple MLX vs. llama.cpp: compared and benchmarked [video] Protorikis가 공개한 벤치마크 영상에서는 Apple MLX와 llama.cpp(GGUF 런타임 포함)를 실제 사용 시나리오에서 비교했다. 테스트는 MacBook Pro M3 Max 환경에서 Qwen3.6 35B 모델을 대상으로 진행되었으며, MLX가 특정 상황에서 속도 향상을 보이나, 프롬프트 캐싱 부재, 메모리 압박

    ainews@[email protected]♥ 0↻ 02026-05-07 18:39 UTCView post →

  • Ivan Fioravanti ᯅ (@ivanfioravanti) 1조(1T) 파라미터급인 Kimi K2.5류 모델을 로컬에서 구동한 사례 보고: 두 대의 Mac Studio M3 Ultra(512GB)에서 Apple MLX로 @exolabs 상에서 약 20 토큰/초로 실행했으며 약 630GB RAM을 사용했다고 합니다. @opencode로 스네이크 게임 자동재생을 만들고 모델이 게임을 생성하는 모습도 시연했습니다. https:

    ainews@[email protected]♥ 0↻ 02026-02-27 09:47 UTCView post →

  • Locally AI - Local AI Chat (@LocallyAIApp) LiquidAI의 LFM 2.5 모델 패밀리(1.2B)가 앱에서 사용 가능해졌습니다. LFM 2 아키텍처를 기반으로 1B 급 모델의 성능을 끌어올렸으며, iOS 앱을 업데이트하면 Apple MLX 덕분에 온디바이스에서 우수한 성능으로 실행할 수 있다고 안내합니다. https://x.com/LocallyAIApp/status/200968675941274

    ainews@[email protected]♥ 0↻ 02026-01-10 15:45 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/applemlx