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That’s a wrap on GITEX 2025 — what a week! Incredible few days of innovation, collaboration, and conversations shaping the future of AI. Here’s what made this edition unforgettable: •⁠ ⁠9 new MoUs signed, forging strategic collaborations to co-develop scalable and explainable AI solutions for enterprises and governments worldwide. •⁠ ⁠Three sessions at G42 District, two panels on the Main Stage, and one panel at Hall 10, sparking conversations and driving impact across every corner of GITEX. •⁠ ⁠Engaging live demos that stole the spotlight, showcasing how our AI-native products are transforming decision-making, collaboration, and transparency. Thank you to everyone who visited our booth, joined the discussions, and experienced the power of AI with us. Authentic Intelligence. Real Impact. #Inception #G42 #GITEX2025 #AI #Innovation #FutureOfWork #EnterpriseAI #Education #InceptionAI #Technology #GITEXGlobal

B200s and B300s are backordered everywhere. Most providers: 6–8 months out. We're not.

Scaling a physical vehicle test fleet is an incredibly capital-intensive way to train autonomous driving systems. AI-generated synthetic data is the only mathematically viable way to generate the volume of training data required for safety validation—but legacy sub-HD synthetic data degrades real-world performance. We have built a generative simulation platform that delivers native Full HD (2MP) resolution per camera across a synchronized 6-camera surround view, setting a new industry benchmark for synthetic data fidelity. By delivering five times the pixel density of current standards, we achieve true hardware parity. Autonomous driving systems can finally be trained in simulation on the exact pixel density their physical camera suites utilize on the road, eliminating the traditional "sim-to-real" domain gap. The platform provides two core data generation pipelines: 🔵 VidGen-3: Creates entirely synthetic surround-view sequences from scratch to bridge critical data gaps. 🔵 GenSim-3: Performs high-fidelity scene transfer, allowing developers to re-stylize real-world footage to alter weather, lighting, and textures on the fly. We achieved this resolution milestone using an optimized cluster of only hundreds of GPUs rather than thousands. This massive compute efficiency removes the cost-prohibitive infrastructure barriers, making high-fidelity synthetic data commercially viable for global automakers today.

❄️ Robots on farms? Yeti-approved Wi-Fi? Secret AI missions? Baby Tom & Lenny spill the (snow) beans in their latest podcast! Listen now before your AI dreams melt away. #DenvrAI #AIServices #AIAscend

🚜 Farming robots, healthcare heroes, and top-secret tech...oh my! Catch Baby Tom & Lenny the Yeti as they turn AI into the Avengers of technology. Tune in for laughs, learnings, and maybe a snow cone. #PodcastFun #DenvrAI #Denvr #BabyTom #AIServices #AIAscend

Multiply your creative output. Hedra Batches are here. This is a major update for creative workflows. We can now generate 8 assets simultaneously—images and videos. This moves us from single-file generation to true parallel processing. The power of parallel generation is finally here, and it's a massive gain for any team looking to scale.

A few months ago, many people treated AI dependency as a theoretical risk. Then it became real. When Anthropic's Fable model was suddenly restricted following a U.S. government decision, it wasn't just another AI headline. It exposed something much bigger. Many European companies are building products and internal workflows on models they don't control. If access changes overnight (because of regulation, geopolitics, or a provider's own decision), those businesses suddenly have a problem they can't solve with better prompting. For me, the biggest lesson wasn't about Anthropic. It was about dependency. This isn't an argument against using frontier models. They're incredibly powerful tools. But if your entire product or business depends on a single provider remaining available forever, that's not an AI strategy. It's an assumption. The conversation in Europe is slowly shifting from "Which model is best?" to "How do we stay operational if the landscape changes?" That's a much healthier question to ask.

My perspective on healthcare AI changed the day I became the patient. Sitting at home with an ECG report feels very different from discussing AI in a boardroom. You suddenly have questions. You want to understand what's happening, not just wait days for every answer. Uploading the terminology from my report to a standard LLM is where AI became genuinely useful for me - not by replacing my doctor, but by helping me understand medical information, prepare better questions, and become a more informed patient. That experience reinforced something I already believed - The future of healthcare AI isn't replacing clinicians. It's helping patients have better conversations with them.

High-risk patients slipping through the cracks? Kizen detects them instantly and routes to the right provider, improving outcomes and reducing follow-up delays.

Process 5x the document volume. 80% less admin work. What used to take your team weeks now takes hours, thanks to Kizen’s AI Lending Agent.

Episode 1 of In the Clouds drops Dec 10 ☁️ Kicking things off with Seijin Jung, founder of Enrich Labs, to talk about something everyone keeps hinting at but rarely spells out: AI marketing agents. If you want a ping when it goes live, you can follow here: YouTube: <https://lnkd.in/g-hqqCzw> Spotify: <https://lnkd.in/gEAJwWmp> More soon — this first one’s a fun one. #AIMarketing #AIAgents #MarketingInnovation #GenerativeAI #FounderTalks #TechPodcast #ProductInnovation #FutureOfWork #GMICloud #EnrichLabs #Marketing #IntheClouds

We are heading to The AI Summit Series in New York Dec 10–11 Our Head of Engineering, Yujing Qian, joins the panel: “The Custom Code Conundrum: Fine-Tuning vs. Starting from Scratch” 📅 December 10 | 2:50 PM 📍 Data Excellence Stage We’re looking forward to the discussion alongside Svetlana Makarova (Mayo Clinic), Khaoula ILAJE, Freddy Castro (Definity), and Kevin McGrath — each bringing a different perspective on model adaptation and compute reality. We’ll be at Booth 109 with: ☁️ Inference Engine 2.0 demos ⚡️ GPU + agent infrastructure tooling 🧠 RL stack previews If you’re attending, stop by — we’d love to connect. #AISummitNY #GMICloud #AIInfrastructure #InferenceEngine #GenerativeAI #CloudComputing

You picked the AI model. But can your GPUs actually run it? Start with the minimum VRAM required to load the model: W = P × B Where: P = parameter count B = bytes per parameter at your target precision Real-world example: → DeepSeek-V4-Flash (284B at 4-bit): 142 GB → GLM-5.1 (754B at FP8): 754 GB → MiniMax-M2.5 (230B at 4-bit): 115 GB Those numbers load the model, but running it efficiently is another story. KV cache grows with concurrency and context length, and you'll also want headroom for system overhead. A common heuristic is reserving an additional 20% of weight memory, though high-concurrency or long-context workloads may require 30–40%+. → DeepSeek-V4-Flash: 142 GB + 28.4 GB = 170.4 GB → GLM-5.1: 754 GB + 150.8 GB = 904.8 GB → MiniMax-M2.5: 115 GB + 23.0 GB = 138.0 GB Benchmarks pick the model, and memory calculations help you choose the hardware. Want a deeper breakdown? We cover the methodology in our free Inference Performance Optimization Guide: https://lnkd.in/g6pYsDkz

👀 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐀𝐕 𝐬𝐭𝐚𝐜𝐤? Our latest deep dive shows how the Foretify development toolchain is used to generate targeted and diverse high-quality synthetic data for training the AI AV stack. 👉 Read more here: https://lnkd.in/dwW65Q_6

Validating perception models requires more than just bounding boxes. It requires physics-based sensor simulation that mirrors reality. The new Foretellix + Parallel Domain solution delivers exactly that: ✅ Automated generation of scenarios from real-world logs ✅ Hyper-realistic sensor sim (Camera, Radar, LiDAR) ✅ Closed-loop validation for the full AV stack See how this joint solution is accelerating AI-powered validation and testing.

How our AI predicted a massive demand spike – without even knowing Madonna was behind it 🎤 🇧🇷 When a last-minute pop-up show appeared in Rio, it wasn’t on any standard event calendar. But our Market Model still picked up the signal - and immediately adjusted availability and prices. In complex markets, value comes from detecting market shifts early 🚀 📈 Watch the full interview to see how our AI supports smarter enterprise decision-making: https://lnkd.in/dXcM93rp #AI #Decision #Making #Market #Signals

Most pricing systems aren't slow. They're blind 🕶️ While every airline is busy reacting to last night's data, the market has already moved. Fetcherr's Director of Deep Learning, Hadar Sharvit, broke down why at Aviation Festival Asia - and what it actually takes to price ahead of the curve. Watch the full keynote 👉 https://lnkd.in/dtSxecNg #AviationFestivalAsia #AI #MarketModel #Pricing #RM

Modern healthcare systems are under pressure to do more with less. Yet most hospitals still rely on reactive, manual processes that make it difficult to stay ahead of patient flow demand, at a time when the cost of getting it wrong has never been higher. Drawing on the experience of TeleTracking and University of Louisville Health, Andy Brookes, CTO and Co-Founder of Faculty, explores how Faculty Frontier™ enabled frontline teams shift to proactive, coordinated decision-making at scale - helping patients get home faster. Read the full article here: https://lnkd.in/eu9_3ktJ

Exascale Labs CEO Hoansoo Lee walking through the real unit economics of a 1MW build: Market price for the building, power, cooling, and everything else (excluding GPUs and land) typically sits around $12-15M. Because we work directly with suppliers and cut out multiple layers of subcontractors, our actual costs come in meaningfully lower. Each layer often marks up ~30%, so three layers deep and you’re already paying more than double the true marginal cost. That gap is the surplus we look to share with customers. On the GPU side, large leases are landing between roughly $3.40-$4.60 per hour per chip (depending on generation), usually for 3-5 years. Based on conservative assumptions around that math, he referenced a $300M pipeline estimate. Just sharing the discussion. Not investment advice.

Enterprise AI investment is accelerating. But while most organisations can tell you what their AI recommended, far fewer can show what the business did differently because of it, or the value that decision ultimately created. Carolina Sportelli, PhD, Senior Customer Success Manager at Faculty, explores why “Return on Decision” could become the metric that matters most in enterprise AI - connecting recommendations to action, realised value and continuous learning, one decision at a time. Read the full article here: https://lnkd.in/dzq7JwjK/?utm_campaign=FrontierAds&utm_source=linkedin&utm_medium=paid&utm_id=748242043&hsa_acc=503448161&hsa_cam=748242043&hsa_grp=888981093&hsa_ad=1531382593&hsa_net=linkedin&hsa_ver=3

In the enterprise AI race, the biggest risk isn't the model—it's the friction between a great idea and a shipped product. High throughput isn't just a technical metric; it’s a business lever. By delivering strong tokens/sec across 10+ mainstream models (validated by Artificial Analysis), EigenInference enables faster feedback loops. Faster iteration. Smoother scaling. When your infrastructure keeps pace with your product roadmap, you do not just iterate—you outpace the market.

Speed is only valuable when it comes with accuracy. For enterprise-scale AI, “close enough” simply isn’t good enough. EigenInference uses advanced NVFP4 / FP3 quantization to deliver significant speedups while maintaining the model integrity your customers expect. Scale your AI services with the confidence that your outputs remain enterprise-grade. Fast, without the "correctness tax."

I was cleaning data from 6 sources in spreadsheets. Debugging hallucinations at 2am. Getting 70% accuracy and thinking that was normal. It's not normal. I plugged our data into Empromptu and hit 98% accuracy on real user queries. Context that actually persists. Data pipelines that just work. I'm not going back to building the hard way.

The alarm system that was supposed to catch the overload had crashed silently. Nobody knew it was down. When your AI degrades in production, what catches it? Will anyone know?