5d ago · 7 min read · Running LLMs on-device means fighting two constraints simultaneously: memory and latency. The KV-cache — the buffer that stores past token representations so the model does not recompute them — is often the bottleneck on both fronts. A paper publishe...
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6d ago · 8 min read · Four gigabytes arrived on your hard drive without a knock. Not malware — a language model. Specifically, a file named weights.bin, sitting inside a directory called OptGuideOnDeviceModel, placed there by the browser most of the world uses to read its...
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May 2 · 10 min read · Google DeepMind released Gemma 4 on April 2, 2026, and two things make it immediately significant: Apache 2.0 licensing and hardware efficiency that no competitor at this capability level matches. Gemma 4’s 31B Dense model ranks third globally among ...
Join discussionMay 2 · 10 min read · On April 1, 2026, ASUS announced the UGen300 — a USB stick-sized AI accelerator that plugs into any USB-C port and delivers 40 AI TOPS of dedicated inference compute at just 2.5 watts. Powered by Hailo’s Hailo-10H processor and backed by 8GB of LPDDR...
Join discussionApr 26 · 13 min read · ## Why On-Device AI Matters More Than Ever in 2026 Two years ago, "on-device AI" meant running a tiny sentiment classifier or waking up a voice assistant. In 2026, it means running 70-billion-parameter language models portably on a laptop, processin...
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Apr 2 · 15 min read · Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output. This release
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Mar 18 · 3 min read · Apple Silicon changed what is possible for local AI. The unified memory architecture means ML models can run on the GPU without copying data between CPU and GPU memory. For a desktop agent that needs to process screen content in real-time, this matte...
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