French startup Kog is taking aim at one of the biggest bottlenecks in modern AI: making inference faster and more efficient. While some have argued that GPUs are not ideal for agentic workflows, Kog believes there is still significant untapped performance available in today’s GPU-based systems.
This matters because inference is where AI systems do their real-world work—responding to users, reasoning through tasks, and powering agents. If Kog can help companies get more output from the same hardware, it could lower the cost of deploying advanced AI products at scale.
Why this is a win
- More efficient AI: Better GPU utilization can make AI services faster and more affordable.
- Support for agentic workflows: Optimizations could help AI agents handle complex, multi-step tasks more effectively.
- Practical infrastructure gains: Improving existing GPU performance may benefit startups and enterprises without requiring entirely new hardware stacks.
Kog’s approach reflects a broader trend in AI: progress is not only coming from bigger models, but also from smarter infrastructure. Making inference more efficient could be a key enabler for the next wave of useful, scalable AI applications.