Cerebras migration · LLM latency optimization
For real-time AI products — voice agents, coding assistants, live chat and support copilots, interactive agents — response latency is the difference between “magic” and “meh.” Cerebras runs inference at 2,000+ tokens/sec versus the ~50–150 tok/s typical of most GPU stacks. When the LLM turn is your bottleneck, that’s the biggest movable slice of your response time.
I run my own live inference service on Cerebras, so I’ve already built and debugged the hard parts firsthand — concurrency, retries, quality-parity checks, fallback. Everything below is async and forwardable: no meeting required, and you keep your own API keys throughout.
You send 10–20 representative prompts plus your current model, latency, and spend. I benchmark them on Cerebras — measured latency (p50/p95), time-to-first-token, tokens/sec — compute your cost delta, run a quality-parity check, and tell you exactly which calls are worth moving, which aren’t, and why. A report you can forward. Honest either way.
Free first step: send me 10 prompts and I’ll benchmark them on Cerebras vs your current setup and email you the measured numbers — no charge, no commitment.
Once the audit shows what’s worth moving, I produce the routing/shim pull requests, key wiring, retry/fallback logic, and an evaluation harness. Your engineer integrates them from a clear handoff guide — I never touch your live system. Your $750 audit credits toward this.
Monthly re-benchmark and savings report as new Cerebras models ship, so you always know whether there’s a faster or cheaper path available.
The proof the migration held up. I run your own evaluation set through both the old path and the Cerebras path, and an independent model from a different family judges quality parity. You receive a signed, tamper-evident certificate you can show customers or investors: “latency cut to X, quality held at parity, independently verified on [date].” The monthly watch keeps re-checking as your traffic and the underlying models drift, and alerts you to any regression. Cancel anytime.
Teams running real-time or interactive AI where slowness is felt: voice and phone agents, coding assistants and agentic IDEs, live chat and support copilots, real-time moderation, and multi-step agents. Especially if you already route through a gateway (Vercel AI, OpenRouter, Portkey) — switching a call is config, not a rewrite.
Cerebras runs inference at 2,000+ tokens/sec versus the ~50–150 tok/s typical of most GPU stacks. When the LLM turn is your bottleneck, that’s the single biggest movable slice of response latency. I never quote a fixed multiple — every number comes from your workload, benchmarked.
No. The audit needs only 10–20 representative prompts plus your current model, latency, and spend. Everything is async. Hands-on integration is delivered as pull requests and a guide your own engineer applies.
No. Bring your own key. You keep your Cerebras and incumbent API keys throughout; I never hold or resell tokens.
Then I tell you not to migrate. If a model is unavailable, quality would regress, or your workload isn’t latency-bound, that’s a real result — and it’s what earns the next referral.
Reply with the one LLM call that hurts most (voice / chat / code-gen / agent step), your current model, and ~10 representative prompts (sanitized — no secrets or PII). I’ll send back measured Cerebras numbers and an honest verdict.
Email me for a free benchmark →
Proof it's real, not a slide: watch a verified burst run live — same question, same independent judge; a correct answer proceeds, a wrong one is caught for $0. A live system I built and run on Cerebras.