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Show HN: Bare-Metal AI – Zero-dependency, sub-millisecond AI engines

Show HN: Bare-Metal AI – Zero-dependency, sub-millisecond AI engines

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In 2026–2027, the paradigm has shifted: Zero-Bloat Architecture: Production edge AI demands sub-millisecond execution, sub-100MB memory footprints, and single-binary or zero-dependency libraries. Hardware Direct Access: Maximum performance from modern CPUs using unrolled SIMD (AVX2, AVX-512, ARM NEON) and dedicated tensor registers without heavy BLAS overhead. Local & Autonomous: AI agents require instant local episodic memory, embedded vector indexing, and token-by-token CPU inference without cloud network latency or API bills. 📊 2026–2027 Benchmark & Footprint Leaderboard Category Project Language Binary Footprint Latency / Throughput Zero External Deps?

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Original publisherGitHub
Canonical URLhttps://github.com/eminsk/awesome-baremetal-ai
Publication timeMon, 14 Sep 2026 12:05:52 +0000
Retrieval time2026-09-14T12:16:51.165Z
Last seen2026-09-14T12:16:51.165Z
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

Opening excerpt (first ~120 words) tap to expand

⚡ Awesome Bare-Metal AI (2026–2027) A curated leaderboard, benchmark index, and definitive guide to ultra-lightweight, zero-dependency, bare-metal AI engines written in pure C, C++, Rust, Zig, and Assembly for local LLMs, edge inference, vector search, and autonomous agents. 🎯 The Bare-Metal AI Manifesto In 2024–2025, deploying AI often meant 2GB Docker images, multi-gigabyte Python runtimes, and complex distributed clusters. In 2026–2027, the paradigm has shifted: Zero-Bloat Architecture: Production edge AI demands sub-millisecond execution, sub-100MB memory footprints, and single-binary or zero-dependency libraries. Hardware Direct Access: Maximum performance from modern CPUs using unrolled SIMD (AVX2, AVX-512, ARM NEON) and dedicated tensor registers without heavy BLAS overhead.

Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.

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