Aditya Jain

We invented a recursive megakernel generator called Hyperkernel, the most complete solution to megakernels and GPU efficiency. Hyperkernel-generated megakernels achieve state of the art performance on 11 different domains: inference (DeepSeek, Kimi and GLM), pretraining (Qwen), hardware fault recovery, robotics (π₀-FAST, ACT) diffusion and real time world models (Matrix-Game), full self driving (OpenPilot), voice models (Moshi), genomics (Evo 2) and protein folding (ESMFold). All of this in a single kernel launch. For context, Hyperkernel generated fully optimized megakernels for robotics, genomics and voice in under 50 minutes, and doubled the efficiency for >5 models generally using only about 60-100 lines of code per megakernel. This speaks to the unparalleled standard for code quality in the automated generations. Our first release today open sources the megakernels themselves. Over the next couple weeks, we will release the entire Hyperkernel stack, along with the complete serving stack for Kimi and GLM (20-30% inference efficiency gains across all concurrencies). Out of the box GPU machinery performs terribly for unorthodox research. We built Hyperkernel only 20 days ago for internal use; but given its multi-modality, we have decided to open source it. Creative research should not be bottlenecked by the lack of dedicated infra teams. Hyperkernels offer a one stop shop infra stack for all research. We would love for the open source community to add to it :) GitHub repo: github.com/Hyperstitioncc/Hy… NOTE: We primarily use NVIDIA B300s, but our kernel generation extends to other hardware (B200s, AMD, TPUs). 1/9 We absolutely crush NVIDIA and achieve the best low latency inference for DeepSeek v4.1 Flash. Compared with NVIDIA’s published vLLM results, Hyperkernel delivers: • 36.7% higher per-user generation speed at concurrency 1. • 28.2% higher per-user speed while serving two users, versus NVIDIA serving one. • 15% higher per user speed while serving four users, versus NVIDIA serving one. • 90.6% higher per-user generation speed at concurrency 4, compared with the highest published C4 AgentX interactivity for TP4 B300s. • 28.5% higher per-user generation speed at concurrency 8, compared with NVIDIA's published result. • 36% faster median per-request decode speed.

For Context : 1 - OpenAI’s stargate is spending 500bn on GPUs, with each Pretraining run, individually costing about $4bn. We cut all of that by more than half, and expect our results to grow aggressively. 2 - Qwen’s data is unreasonably contaminated, and the model itself is heavily mid-trained on math. By all means its an unfair comparison, and we still demolish them. Not only is Feather the worlds best 1.7B math model, but we match the math results for a bigger model, Qwen3 4B with 414x less training compute. 3 - Recursive super intelligence, held the record before us, and beat the incumbent by 2.2seconds. They’ve raised 650million from Google Ventures. Teams celebrate 0.8second leaps. You can only contextualise what a 34 seconds jump really means. The next drop will be public later this month. Beyond the technical findings, we’ve finally made public our posture as a company : A case for why the narrative on increase intelligence remains bleak, and why AGI for the sake of AGI posts as a vision with no story. So much more to come within the month. We’re aggressively challenging what it takes to push the frontier.

Today, we release the first series of our findings. 1 - We cut pretraining costs by 62% at frontier scale (8x Chinchilla), and we further cut all AI inference costs by 30% (Faster decode). 2 - Introducing our model ‘Feather’, which decisively overtakes Qwen 3 as the leading frontier 1.7B math model, using 180x fewer total training tokens, even matching Qwen 4B at potent benchmarks in math. 3 - With Anvil II, our state of the art LLM Optimiser, we record the largest pre-training efficiency jump on the NanoGPT Speedrun, leaping the previous by 34seconds - Our record singularly, is a greater percentage drop than the past 45 World Records combined. The website : hyperstition.cc