Edge AI

Explore Google's open-source ML Drift GPU compute engine for LiteRT, featuring tensor virtualization, 5D tensor support, and optimizations for on-device models.

An in-depth technical review of NVIDIA's new Nemotron 3.5 Super VL and Bolt models, exploring their architecture, edge deployment capabilities, and how developers can scale them efficiently using API aggregators.

An in-depth review of NVIDIA RTX Spark, exploring how its 1 petaflop AI compute and 128GB unified memory empower local LLM execution, developer workflows, and the future of enterprise AI integration.

An in-depth analysis of open-weights multimodal decision models deployed at the edge. Explore model compression, vision-language reasoning pipelines, real-world benchmarks, and hybrid cloud fallback architectures.

Nvidia's strategic $3.5 billion partnership with MediaTek signals a major shift toward edge AI and ARM-based custom silicon, defending its market position against Big Tech's in-house AI chips.

An in-depth exploration of the SOLACE conceptual architecture, analyzing how spectrally-selective photovoltaics, photonic neural networks, and energy-aware CMOS scheduling can enable self-powered edge intelligence.

The official launch of llama.app marks a major milestone for local LLMs, bringing a zero-config web interface to the world's most popular inference engine.

Learn how to optimize and fine-tune small language models (SLMs) like moeinGTS 1.5B for high-speed local inference using LoRA and GGUF quantization.

Google has released Gemma 4 models with Quantization-Aware Training (QAT), enabling 4-bit and 2-bit weights that maintain high accuracy for edge device deployment.

Discover how PFlash achieves a 10x speedup in llama.cpp prefill, the latest Ollama performance updates for Qwen models, and a guide to deploying fine-tuned Llama 3.2 on Android.

An in-depth technical analysis of NVIDIA's new 4B parameter multimodal model, featuring 128k context support for audio, video, and document processing.

An in-depth look at Google's Gemma 4 family, the mechanics of Mixture of Experts (MoE), and how small, edge-capable models are transforming data pipelines and developer workflows.