VSORA

❌ MYTH: More GPUs always mean better AI. Adding accelerators can increase capacity. But it also increases: ➡️ Power consumption ➡️ Memory traffic ➡️ Networking complexity ➡️ Infrastructure costs Beyond a certain point, scale alone cannot compensate for architectural inefficiency. The real question is not how many chips are deployed. It is how much useful inference each chip, and each rack, can deliver. Jotunn8 was designed around workload efficiency, with deterministic latency, high memory capacity and an architecture that scales from a single accelerator to rack-level deployments. 💡 REALITY: Better architecture can create more value than simply adding more hardware. #AIMythBuster #AIInfrastructure #AIInference #Semiconductors #VSORA

🔋 The next bottleneck for AI may not be compute. It may be electricity. Demand for AI is growing faster than the infrastructure required to power it. For data-center operators, every additional watt has an impact on: ➡️ Deployment capacity ➡️ Cooling requirements ➡️ Operating costs ➡️ Infrastructure scalability Energy efficiency is therefore becoming more than a sustainability objective. It is a strategic requirement for scaling AI services. VSORA’s approach is to increase useful inference performance without relying solely on more silicon or larger clusters. Jotunn8 combines runtime orchestration with a register-centric architecture to improve compute efficiency while targeting lower and more predictable latency. #EnergyEfficiency #AIInfrastructure #AIInference #DataCenters #VSORA

Agentic AI is already reshaping infrastructure requirements. The question is no longer whether infrastructure can run a single model fast. It is whether it can run multiple models, agents, modalities and data flows efficiently at the same time. This shift creates a different set of constraints: smaller batches, longer contexts, variable workloads and strict latency requirements. More compute alone is not enough. The architecture must be able to orchestrate and utilize that compute efficiently. Engineered to address these infrastructure challenges, Jotunn8’s architecture maximizes throughput, minimizes unnecessary data movement and delivers low, predictable latency across demanding AI inference workloads. This combination of performance and flexibility makes it well suited to evolving use cases, from Agentic AI and Mixture-of-Experts models to enterprise RAG and multimodal applications, while maintaining efficiency and strong performance per watt as infrastructure scales. #VSORA #Jotunn8 #AgenticAI #AIInfrastructure #Semiconductors

Are today's massive AI infrastructure investments building lasting value or just reinforcing an expensive model built on scale? In his latest "Whatever It Takes" newsletter for Corriere della Sera, Federico Fubini asks exactly that. And it's a question the inference market is already starting to answer. The assumption that every workload needs the largest model on the most power-hungry hardware is breaking down. Cost per token, energy efficiency, latency, and the ability to match the right model to each task now matter as much as raw compute. That's where purpose-built architecture changes the equation. Fubini doesn't hedge on this: he names VSORA among the European companies building AI processors that are, in his words, "ten times more efficient" than Nvidia's in energy consumption. It's precisely the direction we took with Jotunn8: an architecture built around efficient AI execution from the ground up, not technology adapted from a different computing era. Grazie to Federico Fubini for the thoughtful analysis, and to Corriere della Sera for bringing this industrial debate to a wider audience. #VSORA #Jotunn8 #AIInference #AIInfrastructure #Semiconductors #EuropeanTech

💡AI inference doesn’t just have a compute problem. It has an efficiency problem. The industry continues to add processing power. Yet the questions facing infrastructure teams are increasingly practical: 👉 How many users can the system serve? 👉How predictable is latency as workloads change? 👉How much power is required to generate useful output? Peak performance matters. But in production, workload efficiency determines real-world performance. ⚡This principle is at the core of Jotunn8. VSORA designed its architecture specifically for efficient AI inference, combining high compute utilization, deterministic latency, high-bandwidth memory and scalability from chip-level to rack-level deployments. The next generation of AI infrastructure will not be defined by how much compute it contains, but by how effectively that compute is used. How does your organization measure AI infrastructure efficiency? #AIInference #AIInfrastructure #EnergyEfficiency #Semiconductors #VSORA

In her latest interview on Bloomberg Television, Sandra Rivera, Chair of the Board at VSORA, shares her insights into the critical role of AI and data center infrastructure across the economy and in our everyday lives. Her analysis highlights the interconnected ecosystem required to support AI at scale, a context that reinforces the need for innovations such as Jotunn8, VSORA’s purpose-built AI inference accelerator designed to improve efficiency, scalability and performance. 👇Watch the interview from 23:32 to 31:42 and learn more about the infrastructure shaping the future of AI: https://lnkd.in/eARYgGKZ #VSORA #AIInfrastructure #AIInference #DataCenters #Semiconductors #Jotunn8

⚡ AI efficiency cannot be achieved by scaling today’s architectures alone. It requires rethinking them from the ground up. This has long been our conviction, and as enterprise AI moves into real-world deployment, its demands are only making the case stronger. Read the full article by Lauro Rizzatti on EDN for a deeper dive: https://lnkd.in/ewrvss2p

For years, GPUs have been the default choice for AI. But today’s inference workloads are changing: ➡️ Mixture-of-Experts models ➡️ Agentic AI ➡️ Retrieval-Augmented Generation ➡️ Longer context windows These workloads are no longer constrained by compute alone. Data movement, memory efficiency and latency are becoming just as critical. At VSORA, we believe the next generation of AI infrastructure will not come from simply adding more GPUs. It will come from rethinking the architecture itself. That is why Jotunn8 was designed from the ground up for AI inference: maximizing throughput, reducing unnecessary data movement and delivering exceptional performance per watt. AI infrastructure needs more than raw compute. It needs an architecture built for inference. Is today’s AI infrastructure truly ready for tomorrow’s workloads? #AIInference #AIInfrastructure #Semiconductors #Jotunn8 #VSORA

[RECRUTEMENT] 🚀 Façonnez l'avenir de l'IA avec nous ! Chez VSORA, nous repoussons les limites de l'inférence IA grâce à des équipes pluridisciplinaires animées par une même exigence d'excellence. Pour accompagner notre croissance, nous recherchons de nouveaux talents désireux de contribuer à des technologies de pointe et d'avoir un impact concret sur l'avenir de l'intelligence artificielle. Si cette aventure vous inspire, nous serions ravis d'échanger avec vous. 👉 Découvrez nos opportunités et rejoignez l'aventure VSORA: https://lnkd.in/e__hHAZt #VSORA #Recrutement #Ingénierie #IntelligenceArtificielle #IA #AIInference #SemiConducteurs #DeepTech #Carrières

⚡ AI inference is no longer just about delivering more FLOPS. It's about delivering predictable performance, lower cost per token, and infrastructure that scales efficiently from a single accelerator to production-ready racks. 🚀 That's exactly what JOTUNN8 was engineered to deliver. Combining high compute density, 288 GB of HBM memory, 8 TB/s memory bandwidth, deterministic latency, and a CUDA-free architecture, the VSORA AI Inference Platform is designed to accelerate modern AI workloads while keeping efficiency at its core. Whether deploying LLMs, multimodal applications, RAG, or enterprise AI workflows, JOTUNN8 gives organizations the flexibility to scale without compromising performance or power efficiency. #VSORA #JOTUNN8 #AIInference #AIInfrastructure #Semiconductors #DeepTech