Build a marketing landing page for a custom AI inference chip startup targeting cloud providers and AI infrastructure teams.
The page must include:
Hero section:
- Bold headline: "GPUs were built for training. Agents need something different."
- Subheading: agentic AI workloads — multi-step reasoning, tool calls, memory retrieval — have fundamentally different compute patterns than batch inference. This chip is designed from the ground up for the agent era.
- Primary CTA: "Talk to Our Silicon Team"
- Hero visual: a side-by-side architecture comparison showing GPU compute patterns vs. agent-optimized silicon, highlighting KV cache access, branching, and variable context
Problem section:
- Title: "Running agents on GPUs is like running a city on a highway"
- 3 data points: cost per agent step on current GPUs, latency overhead from KV cache misses, energy wasted on workloads GPUs weren't designed for
- Short copy explaining why batch inference chips are a poor fit for interactive, multi-step agent workflows
The opportunity:
- 3-column grid: Lower cost per agent step, Faster tool-call latency, Better energy efficiency at scale
- Supporting stat or quote from a public AI infrastructure benchmark
Architecture highlights:
- 3 technical differentiators (written accessibly): native KV cache architecture, branching-aware execution units, near-memory compute for retrieval
- "Designed for the workloads that matter in 2026 and beyond"
Who it's for:
- 3 customer profiles: Cloud providers, Enterprise AI teams running agents at scale, Edge AI deployment — each with a one-line benefit
Team and credibility:
- "Built by chip architects from [top semiconductor companies]" (placeholder)
- 2–3 placeholder quotes from AI infrastructure leads
Early partner form:
- Name, company, current inference spend, workload type
- "Become a Design Partner" CTA
Footer with company name, tagline, and contact email.
Create a modern startup design inspired by Y Combinator (YC) companies.
Choose one bright primary color and build a clean, minimal color scheme around it.
The design should feel bold, simple, and product-focused with strong typography, generous whitespace, and clear hierarchy.
Builds a marketing landing page for an agent inference chip design platform. Explains why GPU architectures are a poor fit for agentic workloads, the custom silicon opportunity, and a sign-up for chip designers and AI infrastructure teams. Cycle-accurate simulation, RTL design tooling, and tapeout pipelines require specialized engineering infrastructure.