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February 27, 2026
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February 27, 2026
The Tech Trends
AI
AI Ethics
Automation
Deep Learning
Generative AI
Machine Learning
Robotics
Culture
Creator Economy
Digital Nomads
Internet Culture
Remote Work
Tech Careers
Tech Events
Future Trends
5G/6G Networks
BioTech
Metaverse
Quantum Computing
Space Tech
Sustainable Tech
Innovation
AgriTech
EdTech
FinTech
Green Tech
HealthTech
Smart Cities
Gadgets
AR/VR Devices
Drones
Health Tech
Smart Home
Smartphones
Wearables
Software
App Development
Cloud Computing
Cybersecurity
Open Source
Productivity Tools
SaaS
Startups
Disruptive Ideas
Founder Stories
Funding News
Startup Trends
Tech Launches
Unicorn Watch
Web3
Blockchain
Cryptocurrency
DAOs
Decentralization
NFTs
Smart Cities
×
Scaling Inference
The Tech Trends
Scaling Inference
AI
Scaling Inference
Scaling Inference: The Logic of Hybrid Compute Strategies
by
Lina Kovács
February 27, 2026
Table of Contents
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Key Takeaways
Who This Is For
The Inference Bottleneck: Why the Cloud Isn’t Enough
The Rise of the NPU
Defining the Logic of Hybrid Compute Strategies
1. The Tiered Model (Task-Based)
2. The Speculative Model (Cooperative Inference)
3. The Privacy-First Model
The Role of Small Language Models (SLMs)
Technical Levers: Optimizing for the Edge
Model Quantization and Compression
KV Caching
Orchestration Layers
The Economics of Tokens: Why CFOs Love Hybrid AI
Common Mistakes in Scaling Inference
Safety and Ethical Considerations
The Future of Hybrid Compute: What’s Next?
Conclusion
FAQs
1. Is on-device AI always more secure than cloud AI?
2. How much slower is a mobile NPU compared to a cloud GPU?
3. Can I run a 70B parameter model on a smartphone?
4. Does hybrid compute drain the user’s battery?
5. What happens if the user goes offline?
References
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Table of Contents
