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    Nate
    Nate@nate_5126h
    📱Kimi K3📱GitHub💭AI
    Kimi K3 runs on 8GB RAM

    @nate_512WAIT... YOU CAN NOW RUN A TWO POINT SEVEN TRILLION PARAMETER MODEL ON AN 8GB LAPTOP 🤯 The Kimi K3 engine achieves this with a 176KB binary written in portable C. It bypasses memory limits by streaming the 1.56TB weights directly from disk for every single token. → 8GB RAM gets you 26 seconds per token → 128GB RAM gets you 5 seconds per token Same exact math and byte identical results regardless of the machine. Zero GPUs required. This is pure brutalist engineering. Free and open-source. repo in 🧵↓

    元の投稿を見る

    Kimi K3 runs on 8GB RAM

    @nate_512さんの写真· Sep 22, 2026· Kimi K3

    この写真について

    The image is a screenshot of a GitHub repository page. The focus is on a technical description of a large language model called "kimi-k3-in-c". The page details its parameters, memory usage, and a diagram illustrating its architecture. The mood is informative and technical, presented in a clean, code-repository style. A notable visual element is a cartoon of Spongebob Squarepants in the corner, looking surprised or overwhelmed, which adds a touch of humor to the otherwise technical content. The on-screen text includes "github.com", "README", "Contributing", "Apache-2.0 license", "More", "kimi-k3-in-c", "A 2.78-trillion-parameter model. One CPU. 8 GB of RAM.", "Kimi K3 inference in portable C99. No BLAS. No framework. No GPU.", "CI passing", "license", "Apache-2.0", "C99", "portable", "platform", "Linux x86-64", "

    Kimi K3の写真をすべて見るKimi K3のウィキを読む

    ?

    Kimi K3の写真をもっと見る

    Kimi K3の写真をすべて見る
    Kimi K3 2.8T parametersKimi K3 2.8T parametersAndrew Ng Stanford AI Engineering LectureAndrew Ng Stanford AI Engineering LectureKarpathy Stanford AI engineering lectureKarpathy Stanford AI engineering lectureLoop vs graph agents explainedLoop vs graph agents explainedGoogle free graph engineering courseGoogle free graph engineering courseKimi K3 on a single CPU 8 GB RAMKimi K3 on a single CPU 8 GB RAM
    写真
    Nate
    Nate@nate_5126h
    📱Kimi K3📱GitHub💭AI
    Kimi K3 runs on 8GB RAM

    @nate_512WAIT... YOU CAN NOW RUN A TWO POINT SEVEN TRILLION PARAMETER MODEL ON AN 8GB LAPTOP 🤯 The Kimi K3 engine achieves this with a 176KB binary written in portable C. It bypasses memory limits by streaming the 1.56TB weights directly from disk for every single token. → 8GB RAM gets you 26 seconds per token → 128GB RAM gets you 5 seconds per token Same exact math and byte identical results regardless of the machine. Zero GPUs required. This is pure brutalist engineering. Free and open-source. repo in 🧵↓

    元の投稿を見る

    Kimi K3 runs on 8GB RAM

    @nate_512さんの写真· Sep 22, 2026· Kimi K3

    この写真について

    The image is a screenshot of a GitHub repository page. The focus is on a technical description of a large language model called "kimi-k3-in-c". The page details its parameters, memory usage, and a diagram illustrating its architecture. The mood is informative and technical, presented in a clean, code-repository style. A notable visual element is a cartoon of Spongebob Squarepants in the corner, looking surprised or overwhelmed, which adds a touch of humor to the otherwise technical content. The on-screen text includes "github.com", "README", "Contributing", "Apache-2.0 license", "More", "kimi-k3-in-c", "A 2.78-trillion-parameter model. One CPU. 8 GB of RAM.", "Kimi K3 inference in portable C99. No BLAS. No framework. No GPU.", "CI passing", "license", "Apache-2.0", "C99", "portable", "platform", "Linux x86-64", "

    Kimi K3の写真をすべて見るKimi K3のウィキを読む

    ?

    Kimi K3の写真をもっと見る

    Kimi K3の写真をすべて見る
    Kimi K3 2.8T parametersKimi K3 2.8T parametersAndrew Ng Stanford AI Engineering LectureAndrew Ng Stanford AI Engineering LectureKarpathy Stanford AI engineering lectureKarpathy Stanford AI engineering lectureLoop vs graph agents explainedLoop vs graph agents explainedGoogle free graph engineering courseGoogle free graph engineering courseKimi K3 on a single CPU 8 GB RAMKimi K3 on a single CPU 8 GB RAM