AI NewsKog is going deeper to squeeze more inference out of GPUs

Kog is going deeper to squeeze more inference out of GPUs

9:23 PM IST · August 14, 2026

The race for fasterAI inferenceis on, and markets gave Cerebras and its purpose-built chipsa warm welcomein its IPO debut in May. But French startupKogis betting that there’s a lot more power to be squeezed out of conventional GPUs. The startuphit the front page of Hacker Newsin May with atech previewaimed at proving that “extremely fast single-request decoding is possible on the standard datacenter GPUs enterprises already own” — such as the AMD MI300X and Nvidia H200 GPUs it used for its demo. Some were disappointed to hear this didn’t extend to GPUs in our laptops, but others saw the potential. With inference speed and costs now being a critical bottleneck, Kog’s promise to unlock new capabilities on existing hardware with software optimization attractedmore than onlookers. “We had 200 tangible business leads,” CEO Gaël Delalleau told TechCrunch. Based on early feedback, the solo founder expects software engineering to be the first use case. Veteran Claude Code users are well aware that they sometimes have to wait hours to get results. Anthropic itself understands that speed is worth money, and chargesa price multiplefor Claude’s Fast Mode. Kog is hoping to target customers put off by those delays, usually because they rely on AI workflows for professional tasks. But the startup also has design partners that let users generate games and apps with a prompt, and for whom a faster outcome thanks to the Kog Inference Engine (KIE) would mean more revenue, Delalleau said. The company realizes this market is not quite mature yet. While observing demand, Kog learned that its prospective customers aren’t prepared to fine-tune small models. “And that’s why since the launch, we’ve been fully focused on accelerating the development of larger models to meet the demand we’ve seen.” This leaves Kog with a huge leap to make to deliver on its promise of “30x faster LLM inference.” Its demo showed an impressive 3,000 per-request tokens per second (TPS) — but with a purpose-built small model with only some 2 billion parameters, thenow open sourcedLaneformer 2B. Contradicting skeptics, Delalleau is confident the same approach can work just as well with LLMs, whose size can be a challenge for inference chips. “GPUs have a bright future,” he said. For Kog’s CEO, the idea that they aren’t well suited for decoding has become a misconception; newer GPUs have more and more memory bandwidth that only begs to be unlocked. Kog isn’t alone in thinking that software optimization can help GPUs do more than it says on the box.ZML, also from France, released hardware-agnostic software that bypasses Nvidia’s CUDA to support fast inference across competing chips. But Delalleau said Kog is more akin to Stanford University labHazy Research, with an even deeper-level focus on GPU acceleration. Delalleau himself is not a researcher, and his first startup, TechCrunch50 2009 alumStribe, has nothing to do with his new one — other than his former co-founder turned VC Kamel Zeroual, whose firmVarsity VCco-led Kog’s seed round. But the startup’s deep-level focus stems from his unique background. Having studied solid-state physics at France’s École Polytechnique, he went on to work in offensive cybersecurity — also known as white hat hacking. According to Delalleau, this shaped the mindset he is now encouraging his team to adopt. On the science side, “there’s this mindset of understanding the laws of physics, and the laws of the GPU in order to make the most of them.” As for hacking, the four-time finalist atDEFCON’s CTF tournamentsaid it taught him “to reverse-engineer things at a very low level — down to assembly language and binary code — to understand how it works, and to try to use it to achieve a goal for which it wasn’t necessarily designed.” The downside of this approach is that it is very hands-on and time-consuming. “For every new GPU, we’ll dedicate several weeks or even months, to really dig into the details and conduct GPU engineering research on that hardware.” With a team of 11 people, this puts a limit to the number of chips that Kog can work with, at least for the foreseeable future. In the longer run, Kog hopes to feed its methodology into agent-based pipelines that will let it support more chips and models. As Europe seeks to build its own capability on those two fronts, this could add sovereignty tailwinds for the startup, which is alreadysupported by Scalewayand backed by France’sBpifranceandFrench Tech 2030’s program. For now, though, Kog needs to prove to the world that its approach works on LLMs. This will also be key to securing more funding. “Once we’ve implemented our first major model at 10x speed, which I think will be in September, we’ll be able to start demonstrating customer traction and from there, raise our Series A,” Delalleau said.