Kog is going deeper to squeeze more inference out of GPUs

What changed

French startup Kog challenges the common view that GPUs are poorly suited for agentic workflows. Instead, Kog is developing technology to extract more inference power from GPUs, targeting the efficiency of AI agents running complex, multi-step tasks. Their approach digs deeper into GPU capabilities to optimize how AI models execute inference, going beyond current limitations seen in agent-based applications.

Why builders should care

GPUs have been primarily associated with large, monolithic AI model inference or training, while agentic workflows—where AI iteratively processes, reasons, and acts—often rely on CPUs or specialized hardware for logic and orchestration. Kog’s work suggests GPUs can do more in handling these workflows, potentially lowering costs and simplifying infrastructure by consolidating computation on GPUs. That could reduce the need for hybrid systems that split workloads and add latency.

The practical takeaway

If Kog’s technology lives up to its promise, developers and AI operators could squeeze more performance out of existing GPU investments for inference-heavy agents. This means inference models could run faster and cheaper on familiar hardware, improving efficiency without large capital expenses on new chip types or complex setups. It also pressures cloud and AI hardware providers to rethink GPU optimization for agentic workloads rather than writing them off as unsuitable.

What to watch next

Pay attention to Kog’s upcoming software releases or partnerships that demonstrate real-world impact. Watch how major cloud platforms and GPU vendors respond—do they adapt their stacks to support more agentic inference on GPUs? Observe if this shifts the economics of running intelligent agents at scale, especially for startups and businesses currently balancing CPU and GPU costs across AI workflows.

AI Quick Briefs Editorial Desk