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-title: README
-emoji: 🐢
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-sdk: static
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-license: mit
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-[We are a research company that builds AI products for the edge.](https://www.atomgradient.com/)
+  ---
+  ---
+  title: README
+  emoji: ⚡
+  colorFrom: indigo
+  colorTo: purple
+  sdk: static
+  pinned: false
+  license: mit
+  ---
+
+  # AtomGradient — Bringing AI to the Edge
+
+  **We are an independent research group dedicated to making AI run efficiently on edge
+  devices.**
+  We believe powerful AI should be private, accessible, and free from cloud dependency. All our
+  research is open-source.
+
+  🌐 [atomgradient.com](https://atomgradient.com) · 🐙 [GitHub](https://github.com/AtomGradient)
+  · 🚀 [EchoStream AI](https://www.echostream-ai.com/)
+
+  ---
+
+  ## Research
+
+  ### [Prism — Cross-Domain Personal Data Integration on Consumer
+  Hardware](https://atomgradient.github.io/Prism/)
+
+  Integrating finance, diet, mood, and reading data entirely on consumer Apple Silicon, producing
+   emergent cross-domain insights with zero data leakage.
+
+  - 📈 **1.48x** cross-domain insight emergence (IIR)
+  - 🔒 **125.5x** federation compression, zero data leakage
+  - ⚡ **49.9 TPS** real-time inference (35B on M2 Ultra)
+
+  [[GitHub]](https://github.com/AtomGradient/Prism) ·
+  [[Paper]](https://atomgradient.github.io/Prism/)
+
+  ---
+
+  ### [ANE Batch Prefill — On-Device Parallel LLM
+  Inference](https://atomgradient.github.io/hybird-batch-prefill-on-ane/)
+
+  Fused matrix-vector kernels enabling concurrent ANE batch prefill + GPU decode on Apple Silicon
+   for Qwen3.5 models.
+
+  - 🚀 **11.3x** ANE batch prefill speedup (268 tok/s)
+  - 🔋 **79%** power reduction for prefill component
+  - ⏱️  **<30 ms** state transfer overhead
+
+  [[GitHub]](https://github.com/AtomGradient/hybird-batch-prefill-on-ane) ·
+  [[Paper]](https://atomgradient.github.io/hybird-batch-prefill-on-ane/)
+
+  ---
+
+  ### [hybrid-ane-mlx-bench — Disaggregated LLM Inference on Apple
+  Silicon](https://atomgradient.github.io/hybrid-ane-mlx-bench/)
+
+  Benchmarking CoreML ANE prefill + MLX GPU decode for Qwen3.5 on Apple Silicon, with four
+  inference strategies compared.
+
+  - 🔄 ANE prefill matches GPU at **~410 tokens**
+  - 🔋 **282x** GPU power reduction during prefill
+  - 📊 4 inference pipelines benchmarked
+
+  [[GitHub]](https://github.com/AtomGradient/hybrid-ane-mlx-bench) ·
+  [[Paper]](https://atomgradient.github.io/hybrid-ane-mlx-bench/)
+
+  ---
+
+  ### [swift-qwen3-tts — On-Device
+  Text-to-Speech](https://atomgradient.github.io/swift-qwen3-tts/)
+
+  Native Swift implementation of Qwen3 TTS 0.6B for real-time, on-device speech synthesis.
+
+  - 📦 **67%** model compression (2.35 GB → 808 MB)
+  - 🎙️  Real-time synthesis (**RTF 0.68x**)
+  - 🌍 12 languages supported
+
+  [[GitHub]](https://github.com/AtomGradient/swift-qwen3-tts) ·
+  [[Paper]](https://atomgradient.github.io/swift-qwen3-tts/)
+
+  ---
+
+  ### [Gemma-Prune — On-Device Vision Language
+  Model](https://atomgradient.github.io/swift-gemma-cli/)
+
+  Multi-stage compression pipeline for deploying Gemma 3 4B VLM on consumer hardware.
+
+  - 📦 **25%** model compression (2.8 GB → 2.1 GB)
+  - 📝 **110 tok/s** text generation
+  - 🖼️  **3.4x** image processing speedup
+
+  [[GitHub]](https://github.com/AtomGradient/swift-gemma-cli) ·
+  [[Paper]](https://atomgradient.github.io/swift-gemma-cli/)
+
+  ---
+
+  ### [OptMLX — MLX Memory Optimization Research](https://atomgradient.github.io/OptMLX/)
+
+  Exploring memory optimization techniques for the MLX framework on Apple Silicon.
+
+  - ⚡ Up to **20x** faster mmap loading
+  - 🔄 Zero-copy model loading
+  - 📊 Comprehensive benchmarks
+
+  [[GitHub]](https://github.com/AtomGradient/OptMLX) ·
+  [[Paper]](https://atomgradient.github.io/OptMLX/)
+
+  ---
+
+  ## About
+
+  AtomGradient is an independent research group dedicated to making AI run efficiently on edge
+  devices. Our research powers [EchoStream AI](https://www.echostream-ai.com/) — a product line
+  bringing on-device AI capabilities to real-world applications.
+
+  `Edge AI` · `Privacy-First` · `Open Research`
+
+  ---