Wave 79 Content Briefs: AI Hardware and Edge Computing

Four content briefs for DataGate.ch Wave 79, covering AI hardware, edge computing, and the semiconductor landscape.

Brief 1: The GPU Market War — NVIDIA vs AMD vs Custom Silicon

Target keyword: GPU market AI 2026

Word count: 2,500

Angle: Deep analysis of the AI accelerator market: NVIDIA’s dominance, AMD’s challenge, and the rise of custom silicon from Google, Amazon, and Microsoft. Include pricing, performance benchmarks, and market share data.

Key sections:

• Market overview: size, growth, and key players
• NVIDIA H200/B300: architecture, performance, pricing
• AMD MI300X/MI325X: competitive positioning and ROCm ecosystem
• Custom silicon: Google TPU, Amazon Trainium, Microsoft Maia
• Market share analysis and trends
• Buyer’s guide: which chip for which workload

Brief 2: Edge AI and On-Device Inference — The Next Frontier

Target keyword: edge AI inference 2026

Word count: 2,200

Angle: How AI is moving from cloud to device. Cover mobile NPUs, edge accelerators, and the use cases driving on-device inference: privacy, latency, cost, and reliability.

Key sections:

• Why edge AI matters: privacy, latency, cost, reliability
• Mobile NPUs: Apple Neural Engine, Qualcomm, MediaTek, Samsung
• Edge accelerators: NVIDIA Jetson, Intel Core Ultra, AMD Ryzen AI
• Use cases: autonomous vehicles, industrial IoT, smart home, healthcare
• Model optimization for edge: quantization, pruning, distillation
• Edge AI development frameworks and tools

Brief 3: AI Chip Startups — The Disruptors Challenging NVIDIA

Target keyword: AI chip startups 2026

Word count: 2,000

Angle: Profile the most promising AI chip startups: Cerebras, Groq, SambaNova, Etched, Rain AI. Cover their technology differentiation, funding, and competitive positioning.

Key sections:

• Startup landscape overview
• Cerebras: wafer-scale computing
• Groq: language processing units for inference
• SambaNova: reconfigurable dataflow
• Etched: single-purpose transformer chips
• Rain AI: analog AI computing
• Investment trends and market outlook

Brief 4: Building an AI Hardware Comparison Tool

Target keyword: AI hardware comparison tool

Word count: 2,000

Angle: Practical guide to building an interactive AI hardware comparison tool. Cover data collection, feature comparison matrices, pricing normalization, and performance benchmarking.

Key sections:

• Why comparison tools matter for AI hardware buying decisions
• Data sources: specs, benchmarks, pricing
• Comparison dimensions: compute, memory, bandwidth, power, price
• Building the tool: HTML/JS interactive matrix
• Normalization: performance per dollar, performance per watt
• Keeping data current: automated updates

Publishing Plan