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ml-explore/mlx

MLX: An array framework for Apple silicon

Visit GitHub
C++
29.1k
2.4k
261 contributors
MIT License
website
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Momentum

How fast ml-explore/mlx is gaining stars, and where it lands on Trendshift and GitHub Trending

Activity and trending

Stars, forks and merged pull requests each day or month, lined up with the repository's rank on Trendshift Trending

Sep 6 – Nov 5, 2026

Stars +749 in 61 days

0102030
26

Forks +212 in 61 days

051015
11

Merged PRs 222 in 61 days

051015
13

Trendshift Trendingwhere the repository ranked each day it made the top 25

Not in the Trendshift top 25 in this period

Sep 6Sep 14Sep 22Sep 30Oct 8Oct 16Oct 24Nov 1
The left and right arrow keys step from day to day; Home and End go to the first and the last

Stars, forks, merged pull requests and Trendshift Trending rank, by day
DayStars gainedForks gainedMerged PRs
Sun, Sep 6, 2026+14+11
Mon, Sep 7, 2026+14+33
Tue, Sep 8, 2026+16+78
Wed, Sep 9, 2026+14+12
Thu, Sep 10, 2026+9+22
Fri, Sep 11, 2026+9+64
Sat, Sep 12, 2026+11+11
Sun, Sep 13, 2026+10+30
Mon, Sep 14, 2026+16+34
Tue, Sep 15, 2026+5+72
Wed, Sep 16, 2026+13+01
Thu, Sep 17, 2026+7+31
Fri, Sep 18, 2026+17+50
Sat, Sep 19, 2026+17+70
Sun, Sep 20, 2026+13+00
Mon, Sep 21, 2026+18+50
Tue, Sep 22, 2026+9+50
Wed, Sep 23, 2026+10+30
Thu, Sep 24, 2026+9+11
Fri, Sep 25, 2026+13+33
Sat, Sep 26, 2026+10+63
Sun, Sep 27, 2026+9+13
Mon, Sep 28, 2026+19+59
Tue, Sep 29, 2026+12+77
Wed, Sep 30, 2026+7+411
Thu, Oct 1, 2026+9+012
Fri, Oct 2, 2026+10+410
Sat, Oct 3, 2026+7+51
Sun, Oct 4, 2026+12+14
Mon, Oct 5, 2026+10+56
Tue, Oct 6, 2026+11+23
Wed, Oct 7, 2026+12+48
Thu, Oct 8, 2026+13+54
Fri, Oct 9, 2026+13+313
Sat, Oct 10, 2026+8+27
Sun, Oct 11, 2026+9+40
Mon, Oct 12, 2026+11+35
Tue, Oct 13, 2026+26+21
Wed, Oct 14, 2026+16+25
Thu, Oct 15, 2026+22+70
Fri, Oct 16, 2026+15+37
Sat, Oct 17, 2026+14+50
Sun, Oct 18, 2026+8+00
Mon, Oct 19, 2026+8+25
Tue, Oct 20, 2026+15+71
Wed, Oct 21, 2026+20+41
Thu, Oct 22, 2026+11+111
Fri, Oct 23, 2026+12+311
Sat, Oct 24, 2026+12+40
Sun, Oct 25, 2026+22+55
Mon, Oct 26, 2026+10+27
Tue, Oct 27, 2026+7+46
Wed, Oct 28, 2026+11+44
Thu, Oct 29, 2026+7+37
Fri, Oct 30, 2026+7+43
Sat, Oct 31, 2026+9+17
Sun, Nov 1, 2026+18+44
Mon, Nov 2, 2026+23+15
Tue, Nov 3, 2026+12+43
Wed, Nov 4, 2026+17+30
Thu, Nov 5, 2026+1+00
Total+749+212222
  • 1#1 on the list
  • 7Rank that day
  • Not trending
1

    • GitHub trending history

    Where the repository ranked on GitHub Trending, one dot for each day it was on a list

    Dec 8, 2023 – Mar 24, 2025#1 on 1 day

    #1#5#10#15#20#25
    Top 10
    Jan 2024JulJan 2025JulJan 2026Jul
    The left and right arrow keys step from day to day; Home and End go to the first and the last

    Mentions and achievements

    Where people are talking about ml-explore/mlx, and every badge it has earned
    1. Nov 21 post

      • 4. llama.cpp → Runs LLMs across CPUs, GPUs, and Apple Silicon. → Supports GGUF quantized models for memory-efficient inference. → Great for local AI, edge devices, and running models on consumer…

        Read full on X, posted by @goyalshaliniuk

        @goyalshaliniuk · x.com

    2. 20 days earlier
    3. Oct 131 post

      • Only then compare benchmarks. Use the exact model, quantization, context and app you plan to run. Track prompt processing, generation speed, memory use, power/noise, and whether the model stays fully…

        Read full on X, posted by @supdeck1

        @supdeck1 · x.com

    4. 1 month earlier
    5. Sep 31 post

      • Megathread for listing latest open source projects, research papers that are helping optimizations, efficiencies and accessibility to Open Source LLM and related hardware, software ?

        Read full on Reddit

        r/LocalLLaMA

    6. 9 days earlier
    7. Aug 251 post

      • 19/20 Power is a facility/nameplate envelope, not measured K3 draw. kW: M5 x3 1.44; M5 x4 1.92; M3 x5 2.40; Spark x18 4.32; B300 15; RTX 6000B x24 ~20; H200 x16 20.4; B200 x16 28.6. M3 uses the…

        Read full on X, posted by @audexdev

        @audexdev · x.com

    8. 10 days earlier
    9. Aug 151 post

      • So #Local #models run my #community and #volunteer work: cheaper, and client data stays off the labs. New @Alibaba_Qwen 3.8-27B dropped. I upgraded overnight, additively: new model on its own port,…

        Read full on X, posted by @fabianwilliams

        @fabianwilliams · x.com

    10. Aug 131 post

      • MLX quantizer stops at 2-bit. llama.cpp packs 5 ternary weights into one byte (TQ1_0), been building custom packers to push MLX lower, spend the bits where they matter, ternary/1-bit the rest. + to…

        Read full on X, posted by @MagnusLdt

        @MagnusLdt · x.com

    11. 14 days earlier
    12. Jul 301 post

      • 如果你正在折腾 AI、LLM、Agent 或机器学习项目，这份清单应该能帮你少走很多弯路。GAS哥整理了30个开发者真正高频使用的开源仓库，按功能分类，方便你快速找到需要的那个。 核心开发框架 LangChain — 构建 AI Agent、RAG 应用和 LLM 工作流的首选框架，生态最完善。 → LlamaIndex — 把私有数据（文档、数据库、API）连接到 LLM，专攻 RAG 场景。…

        Read full on X, posted by @cevenif

        @cevenif · x.com

    13. Jul 251 post

      • Building ML systems on Apple silicon shouldn’t require shuffling arrays between CPU and GPU memory. MLX is a machine learning array framework from Apple machine learning research for researchers and…

        Read full on X, posted by @DanKornas

        @DanKornas · x.com

    14. 9 days earlier
    15. Jul 161 post

      • Gemma-4 12B now runs 2.1x faster on Apple Silicon with mlx-dspark, up from 1.8x, same code, same lossless output. Credit to mlx 0.32's new kernels! Also new in v0.4: @PrismML's ternary Bonsai-27B now…

        Read full on X, posted by @_ARahim_

        @_ARahim_ · x.com

    16. Jul 151 post

      • Bonsai-27B & Ternary-Bonsai-27B - Updates (on PRs)

        Read full on Reddit

        r/LocalLLaMA

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