Lovable Partners With Cerebras to Cut AI Response Times, Targeting Major Gains by 2027
Lovable announced a partnership with Cerebras Systems to move its most latency-sensitive AI workloads onto Cerebras’s wafer-scale inference hardware, with the goal of dramatically cutting response times in Lovable’s build-and-iterate loop by 2027.
Details
- The problem being addressed: Traditional AI inference splits a model across many chips, introducing communication delays between them — what the post calls a “communication tax” that slows down the iterative describe-build-review workflow central to using Lovable
- Cerebras’s approach: Cerebras’s Wafer-Scale Engine fits an entire model onto a single silicon wafer rather than distributing it across multiple chips, eliminating much of that inter-chip communication overhead
- Rollout: Lovable will move its “most latency-sensitive workloads” to dedicated Cerebras capacity first; the company did not disclose which specific features move first
- Timeline: The companies are targeting “dramatically reduced response times by 2027,” framed as a multi-phase effort with progress updates to follow, rather than an immediate change
- Quotes: Lovable CEO Anton Osika said “Fast AI is more valuable than slow AI. Creators don’t want to wait.” Cerebras CEO Andrew Feldman said “Lovable has already put building software into the hands of millions of people”
- Context: Cerebras has been striking similar latency-focused partnerships across the AI coding space — for example, Cognition’s Devin already runs select workloads on Cerebras at roughly 1,000 tokens/sec
What happened next
- No immediate user-facing change ships with this announcement; it is a multi-year infrastructure commitment
- Lovable says it will share progress updates as the Cerebras-powered workloads roll out
- See Lovable’s official blog post at lovable.dev/blog/cerebras-partnership for full details