AtomGradient — Bringing AI to the Edge

Research-driven on-device AI company building continual-learning and memory infrastructure.

Neural Imprint lets a model learn from local experience while base weights stay frozen and data never leaves the device.

🌐 atomgradient.com

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Research

Prism — Cross-Domain Personal Data Integration on Consumer Hardware

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] · [Paper]

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ANE Batch Prefill — On-Device Parallel LLM Inference

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] · [Paper]

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hybrid-ane-mlx-bench — Disaggregated LLM Inference on Apple Silicon

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] · [Paper]

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swift-qwen3-tts — On-Device Text-to-Speech

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] · [Paper]

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Gemma-Prune — On-Device Vision Language Model

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] · [Paper]

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OptMLX — MLX Memory Optimization Research

Exploring memory optimization techniques for the MLX framework on Apple Silicon.

• ⚡ Up to 20x faster mmap loading
• 🔄 Zero-copy model loading
• 📊 Comprehensive benchmarks

[GitHub] · [Paper]

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About

AtomGradient is an independent research group dedicated to making AI run efficiently on edge devices. Our research powers EchoStream AI — a product line bringing on-device AI capabilities to real-world applications.

Edge AI · Privacy-First · Open Research