TerminalOne🦈

As you remember, today's article topic will be @simdcompute. Overview > @simdcompute is redefining the future of CFD by compressing processes that once required massive hardware and months of compute into minutes powered by distributed GPU infrastructure. No more expensive servers, no complicated setup. Just upload your mesh, set the physics, and get high-accuracy results at GPU speed. Git >github.com/simd-ai @simdcompute has been accepted into NVIDIA's Inception program, joining a global group of deep tech AI startups. Proof Team analysis The team consists of doxed and 3 core computer science developers. Sam Chien is an AI machine learning engineer Bayangmbe Mounmo is a software engineer Aditya Seshaditya is a data scientist. linkedin.com/in/sch-314/ linkedin.com/in/bayangmbemo… linkedin.com/in/a-seshadity… Tech analysis > This model emerges at the intersection of software + GPU compute-as-a-service, delivering a massive efficiency boost for industrial R&D teams and fast-iteration deep-tech startups. From cryogenic propellant flows to thermal management systems, from aerodynamics to heat-exchanger design, SIMD reshapes the performance–cost–speed equation for the heaviest CFD workloads. > For investors, this is a rare window: SIMD is not just a tool it’s an entry into a multi-billion-dollar market shift. As GPU economics scale and engineering simulation moves to the cloud, the transition is inevitable. SIMD is positioned at the front line of this transformation across HPC, engineering simulation, AI-driven computation, and next-gen energy and aerospace technologies. > Early access is open. This is a genuine ground-floor opportunity for funds, deep-tech investors, and strategic partners who want to get in ahead of the curve. ROADMAP Phase 1 is product demos (simulation in cryogenic field), whitepaper v1, simulation playground v1, and onboarding community. Phase 2: SIMD reward system They will have the GPU provider register its GPU node after running some benchmarks, then accept jobs, submit results, and earn rewards. GPU owners provide their GPU and get SIMD tokens for the job done. Reward = GPU recency + time + simulation type Phase 3: Physics-like shaders Some jobs are repetitive and have similar simulation setups; for that, they use templates. Faster iteration loop, and the pipeline should be smooth from request to result Phase 4: Verification + fairness Phase 5: AI acceleration using a surrogate model. This is not replacing high fidelity simulation - accelerating iteration: quick preview >> It's worth reminding. They'll be doing Space at X on Friday. Final evaluation When we examine all the details, we can conclude that the project is at an early stage $SIMD When I first mentioned the project yesterday, it was valued at 200k mc The team is developing a business in an important and challenging area. It could become a significant alpha player. #CFD #GPU #HPC #DeepTech #Engineering #Startup #Investing #FutureTech