The next AI bottleneck may not be compute. It may be electricity. As AI infrastructure expands, data centers are placing enormous new demands on electrical grids. And much of the conversation focuses on how to generate and deliver enough power to keep up. But there’s another side of the equation: What if AI required less electricity to do the work in the first place? Our latest podcast explores how data movement contributes to AI’s power demands—and why GSI Technology’s Gemini-II APU takes a fundamentally different approach. By bringing computation closer to where data resides, compute-in-memory can reduce unnecessary data movement, opening the door to greater efficiency for workloads that fit the architecture. Because every watt you don't consume is a watt you don't have to generate, transmit, cool, or pay for. Hear why performance per watt could become one of the defining metrics of the AI era: gsitechnology.com/ais-hidden…] #AIInfrastructure #DataCenters #Energy #ComputeInMemory #GeminiII #SustainableAI

As cities become more connected, the real challenge isn't collecting data—it's turning that data into actionable intelligence in real time. This insightful Forbes article by GSI Technology's CEO, Lee-Lean Shu, explores why the promise of smart cities is finally becoming a reality, thanks to advances in AI, edge computing, and intelligent infrastructure. From public safety and traffic optimization to infrastructure monitoring and energy management, the next generation of cities will depend on AI that can make decisions where the data is created—not seconds later in the cloud. It's a worthwhile read for anyone interested in the future of urban technology. Read the article: forbes.com/councils/forbeste… #SmartCities #EdgeAI #ArtificialIntelligence #SmartInfrastructure #IoT #UrbanInnovation #FutureCities

Smart cities aren't about futuristic gadgets—they're about making the infrastructure we already have intelligent. In this episode, we explore how AI, computer vision, and edge computing are transforming cameras, sensors, and connected infrastructure into real-time awareness systems that can improve public safety, optimize transportation, reduce costs, and help cities respond faster when every second counts. The smart city revolution isn't coming—it's already underway. Listen now to learn how AI at the edge is turning data into awareness and reshaping the cities of tomorrow. gsitechnology.com/the-quiet-… #AI #SmartCities #EdgeAI #ComputerVision #IoT #Infrastructure #PublicSafety #Innovation #ArtificialIntelligence

What happens when AI reacts too slowly? In customer service, it creates frustration. In physical AI—robots, drones, autonomous systems—it can create safety risks. The difference between a robot avoiding an obstacle and colliding with it often comes down to one metric: Time-to-First-Token (TTFT). This article explores why AI reaction speed matters, the critical latency thresholds that govern real-world machine behavior, and why the future of physical AI depends on delivering awareness in seconds—not minutes. From microsecond motor control to sub-3-second situational awareness, learn how next-generation compute architectures are enabling safer, more intelligent autonomous systems. Read the full article: forbes.com/councils/forbeste… #ArtificialIntelligence #PhysicalAI #Robotics #EdgeAI #AutonomousSystems #MachineLearning #AIInfrastructure #SmartManufacturing #Innovation

Smart cities don’t need more data. They need better awareness. In our latest podcast, we discuss how cities are shifting from passive monitoring to real-time, instructed observation using edge AI and sensor fusion. Learn how GSI Technology’s APU helps identify critical events faster—from public safety threats to infrastructure failures—while enabling low-power, scalable deployment close to the data. #SmartCities #EdgeAI #AI #PublicSafety #SensorFusion #Infrastructure #Semiconductor #APU gsitechnology.com/smart-citi…

Discover why time to first token (TTFT) is the make-or-break metric for edge AI—and how a breakthrough in compute-in-memory could unlock the next generation of real-time intelligent systems. In this article, Lee-Lean Shu, CEO of GSI Technology, breaks down the root causes of latency and explains how a new architectural approach is reshaping what’s possible at the edge. forbes.com/councils/forbeste…

Latency isn’t just “fast vs. slow” — it’s the brain architecture of Physical AI. In our latest episode, we break down the 3 critical latency layers that turn robots from reactive machines into intelligent agents: ⚡ Sub-15ms → reflex-level control 🧭 Sub-50ms → real-time navigation 🧠 Sub-3s → awareness & reasoning If you care about robotics, AI, or the future of autonomous systems… this is a must-listen. gsitechnology.com/understand…

Most AI chips move data back and forth. What if you could compute where the data already lives? In episode 12 of our podcast, we break down how @gsitechnology's Gemini-II compute-in-memory architecture slashes data movement and unlocks real edge AI performance. If you care about bandwidth, power, and faster time-to-first-token — this one’s worth your time. gsitechnology.com/compute-in…

Matrix math is cheap. Moving matrices isn’t. Learn how GSI Technology's Gemini-II computes directly in memory and why this matters for multimodal LLM TTFT and edge AI systems. bit.ly/4qJghsI

Edge AI isn’t coming—it’s already here. From CES-deployed robots to autonomous vehicles, this episode dives deep into why time-to-first-token (TTFT) is now the metric that matters and how GSI Technology's Gemini-II APU delivers real-time, multimodal AI at a fraction of the power of GPUs. If you care about latency, architecture, and AI that actually works at the edge, Episode 11 of our podcast is a must-listen. bit.ly/4a3wfaK

AI is moving beyond the cloud and into the real world. Lee-Lean Shu, CEO of @gsitechnology , explores how in-memory computing overcomes the power and performance limits of traditional architectures to bring data center–level AI to the edge—enabling real-time decision-making in applications like agriculture, drones, satellites, and defense. bit.ly/45Ft5IG

Episode 10 is live! AI is moving out of the cloud and into the real world. From edge inference to multimodal intelligence, we explore how AI is becoming faster, smarter, and more autonomous—right where data is created. Join us as as we break down the evolution of AI at the edge, why time-to-first-token matters, and what this shift means for drones, vehicles, and remote systems. Listen now and see why the future of AI isn’t centralized—it’s embedded. #AI gsitechnology.com/ai-at-the-…

What if satellites could think? In our latest episode, we explore how @gsitechnology's Gemini-II APU enables on-orbit SAR, change detection, object ID, and more! Listen today! bit.ly/3JZunqx

In Episode 8 of our podcast, we dive deep into the world of true autonomy at the edge, powered by @gsitechnology's Gemini® APU. From navigating dense cities to seeing through storms and darkness with Synthetic Aperture Radar (SAR), to running multimodal LLMs on-board, this chip is redefining what's possible for autonomous systems. #EdgeAI #LLMs gsitechnology.com/revolution…

Cornell University's research on @gsitechnology's APU revealed Compute-in-Memory performance that’s…honestly a little unsettling. - Lightning-fast vector search - Navigating like it has spooky sixth sense - Performing through digital “fog” that would leave GPUs trembling Cornell opened the lab door. GSI built what walked out. #EdgeAI bit.ly/3WxHRwo Happy Halloween!

Independent research by Cornell shows that the Gemini-I APU delivered comparable throughput to GPUs while delivering over 98% lower energy consumption. The APU’s unique design allows it to shorten total processing time by up to 80% compared to CPUs. bit.ly/4olaRD6

Time to First Token (TTFT) is key for LLM performance on edge devices. In our latest podcast, we dive into how @gsitechnology's APU is redefining efficiency at the edge. #AI #EdgeComputing Tune in to learn more: bit.ly/4hjAFgM

Listen to our fifth podcast where we discuss how @gsitechnology's approach to true compute-in-memory represents something revolutionary. It’s not just an incremental improvement—it’s a fundamental rethinking of how computation should work. #AI #Compute bit.ly/4nSux1H