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Normal Computing
AI co-designed silicon for advanced institutions
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3.6MB · 7,364px tall · uses Abcdiatype, Abcdiatypemono, Tobias
Industry
AI Infrastructure, Devtools, Industrial Design
Style
Minimal, Typographic, Scroll Animation
Stage
Series A
Built with
Webflow
About the site
Normal Computing is building AI hardware from co-designed silicon and novel device physics. Oversized statements, technical imagery, and dense performance claims are broken into clear modules, with motion and type contrast adding energy. A strong example of deep tech turning hardware specs and research into an accessible brand narrative.
How this page sells
For chip and AI infrastructure leaders: deep tech pitched like a manifesto, red claims backed by stats and an elite team.
Primary navigation
- About
- Research
- Writing
- Solutions
- EDA
- Careers
ClickTap a section to see it on the page
'A new paradigm of AI hardware to power human progress.' over dark water and a black N monolith, above a five-cell bar: In Production, 2x Faster, Reference Systems, $85M+ (round led by Samsung Catalyst), Intelligence / $ / W.
Why it converts: The bar answers the three questions a hardware buyer asks of a startup (is it real, is it faster, who backs it) before they read a single paragraph.
'AI-accelerated co-design for silicon engineering teams.' with the key phrase in red, then four Normal EDA screens: the design Ontology, spec-to-simulation planning, and an agent that root-causes a failing regression and proposes a fix.
Why it converts: Chip teams lose months to verification. Showing an agent debugging a real regression makes a research claim look like a tool they could deploy on-premises now.
The N block half-buried in rippled sand with one line: 'The semiconductor industry's leading companies use Normal EDA to scale new hardware and accelerate time-to-market.' and Get in touch.
Why it converts: Unnamed but 'leading' customers suit an industry built on NDAs, and the calm full-bleed image gives the reader a pause before the heavier physics.
A red circuit-board render beside 'compute the way physical systems do: noise as a resource, compute with memory, asynchronous', a 10-100x inference-per-dollar-per-watt target, and 'Reference system available. Talk to our engineers'.
Why it converts: Per-dollar, per-watt efficiency is the metric data-centre buyers are squeezed on, and 'pilot deployments open, prioritised by impact' makes a trial feel scarce.
'Frontier AI models are converging to physics. We close the gap between software and hardware' above a grid of red technical diagrams: stateful, stochastic, asynchronous, long context, video diffusion.
Why it converts: It frames the company as riding an inevitable shift rather than selling a niche chip, which is the story investors and strategic partners buy into.
Two dense paragraphs of pedigree: co-creators of TensorFlow frameworks, founders of Meta's Probability team, NISQ pioneers at Los Alamos, and silicon engineers from NVIDIA, Apple and Graphcore.
Why it converts: Deep-tech buyers bet on teams. Naming the exact labs and chips people shipped lets an expert reader verify the credentials instead of taking them on trust.
A dated list of research posts ('CN101: A Digital Thermodynamic Computer for Generative AI', 'AI Inference Needs New Hardware') ending with a Fortune exclusive on the $50M Samsung Catalyst raise.
Why it converts: Regular technical publishing proves the science is progressing, and the funding headline reassures partners that the company will be around to deliver.
A giant NORMAL wordmark filling a red band, with Partner with us, a newsletter sign-up and links to research and careers.
Why it converts: The confident sign-off repeats the single partnership ask, and the newsletter keeps long-cycle hardware buyers in touch until they're ready.
