Францын Kog стартап программ хангамжийн оновчлолоор дамжуулан дата төвийн стандарт GPU-ийн ажиллах хурдыг эрс сайжруулах шийдэл боловсруулж байна.
Хиймэл оюун ухааны дүгнэлт (inference) хийх хурд болон өртөг нь салбарын гол асуудал болоод буй энэ үед Kog компани AMD MI300X болон NVIDIA H200 зэрэг төхөөрөмжүүдийн хүчин чадлыг бүрэн дүүрэн ашиглах технологийн туршилтаа танилцууллаа. Тус компани өөрийн Kog Inference Engine (KIE) хөдөлгүүрээр дамжуулан том хэлний загваруудын (LLM) ажиллах хурдыг 30 дахин нэмэгдүүлэх зорилт тавин ажиллаж байна. Одоогоор 2 тэрбум параметр бүхий Laneformer 2B загварыг ашиглан хийсэн демо үзүүлбэрээр секундэд 3,000 токен боловсруулах амжилт үзүүлээд байгаа юм.
Тус компанийн гүйцэтгэх захирал Гаэль Делаллогийн тайлбарласнаар, GPU-ийн санах ойн зурвасын өргөнийг программ хангамжийн түвшинд нарийн оновчлох нь хамгийн чухал ач холбогдолтой. Физикийн ухаан болон кибер аюулгүй байдлын салбарт ажиллаж байсан туршлагадаа үндэслэн багийнхан GPU-ийн дотоод бүтцийг задлан шинжилж, стандарт бус аргаар ажиллах боломжийг бүрдүүлж байна. Энэхүү арга барил нь тоног төхөөрөмжийн нарийн ширийн зүйлсийг судлахад ихээхэн цаг хугацаа шаарддаг ч ирээдүйд илүү өргөн хүрээний чип болон загваруудыг дэмжих суурь болох юм.
Kog стартап нь одоогоор 11 хүний бүрэлдэхүүнтэйгээр үйл ажиллагаа явуулж байгаа бөгөөд Францын Bpifrance болон French Tech 2030 хөтөлбөрөөс дэмжлэг авчээ. Тус компани есдүгээр сард том хэлний загваруудыг 10 дахин хурдасгасан үр дүнгээ харуулахаар төлөвлөж байгаа бөгөөд үүний дараа Series A шатны санхүүжилт татах зорилготой байна. Энэхүү шийдэл нь AI ажлын урсгалыг мэргэжлийн түвшинд ашигладаг бизнесүүдэд томоохон хэмнэлт авчрах боломжтой гэж үзэж байна.
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The race for faster AI inference is on, and markets gave Cerebras and its purpose-built chips a warm welcome in its IPO debut in May. But French startup Kog is betting that there’s a lot more power to be squeezed out of conventional GPUs.
The startup hit the front page of Hacker News in May with a tech preview aimed 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 attracted more 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 charges a price multiple for 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, the now open-sourced Laneformer 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 lab Hazy Research, with an even deeper-level focus on GPU acceleration.
Delalleau himself is not a researcher, and his first startup, TechCrunch50 2009 alum Stribe, has nothing to do with his new one — other than his former cofounder turned VC Kamel Zeroual, whose firm Varsity VC co-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 at at DEF CON’s CTF tournament said 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 already supported by Scaleway and backed by France’s Bpifrance and French 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.
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