The future of AI demands hardware that can handle the computational intensity of world models, reasoning, and planning. Reasoning in abstract space is going to be computationally expensive at runtime. Understanding the true economics of AI deployment and how specialized hardware delivers superior ROI.
Why traditional approaches are more expensive than they appear
How superior performance translates to business value
Calculate your potential savings with our ASIC-powered servers
Real-world results from our customers
Major online retailer replaced 50 GPU servers with 12 of our InferenceOne Pro servers for their real-time recommendation engine.
"The performance improvement directly translated to increased conversion rates. The faster recommendations helped us increase sales by 2.3%."
Fast-growing AI startup reduced infrastructure costs while scaling from 1M to 10M daily queries using our hybrid training/inference solution.
"We scaled 10x without increasing our infrastructure budget. The hybrid approach let us train and serve models on the same hardware efficiently."
Reducing AI's carbon footprint through efficient hardware
Industry leaders highlight the need for efficient inference and training hardware in 2025 and beyond
"We're going to need all the competition we can get... This kind of reasoning in abstract space is going to be computationally expensive at runtime." - Yann LeCun, Chief AI Scientist at Meta
As of 2025, with the release of V-JEPA 2 in June, AI is advancing towards more sophisticated world models and reasoning systems. These developments, including JEPA for RL and self-supervised learning, require massive computational resources for training and inference. Our ASIC-powered servers deliver the efficiency needed, cutting energy costs by up to 67% while providing high throughput for scaling these advanced models cost-effectively.
"As of yesterday, there have been over one billion downloads of LLaMA... Foundation models will be open source and trained in a distributed fashion." - Yann LeCun
By April 2025, LLaMA models have surpassed 1.2 billion downloads, fueling an ecosystem of startups and enterprises fine-tuning models on-premises. This surge in open-source AI adoption increases the need for versatile hardware. Our hybrid training/inference servers support seamless scaling, enabling businesses to capitalize on this revolution with lower costs and faster ROI.
"This required boiling a small lake... The alternative we have now is a project called VJA, and it seems to work really well." - Yann LeCun on video training challenges
In 2025, advances like V-JEPA 2 and new benchmarks for video world models with long-term spatial memory are pushing boundaries in video prediction and embodied AI. These "lake-boiling" compute demands make traditional GPUs inefficient, but our specialized ASIC servers optimize for such workloads, offering 3x energy efficiency and enabling innovations in autonomous systems without skyrocketing costs.
"System 2 is computationally expensive... We need a different architecture for System 2." - Yann LeCun
Latest 2025 research on System 2 reasoning emphasizes alignment, advanced planning, and deliberate cognition in AI. Papers highlight the need for architectures that balance intensive training with efficient inference. Our solutions shine here, powering complex model training while delivering low-latency, cost-effective inference for emerging applications in medicine, driving, and decision-making.
Join the growing number of companies that have reduced their AI infrastructure costs by up to 60% with our solutions.