Blog/Tech Stack

Cerebras vs GPUs: Why We Switched to Wafer-Scale Inference

We tested Cerebras inference against traditional GPU providers. 15x faster, instant responses, and no compromises on model size.

What Is Cerebras?

Cerebras builds massive AI processors — not chips, but entire wafers. Their CS-3 system delivers performance that clusters of GPUs can't match.

For inference specifically, Cerebras offers 3,000+ tokens per second. That's 10-15x faster than typical GPU-based inference services.

Our Benchmark: GPT-OSS-120B at 3,000 Tokens/Sec

We tested Cerebras with OpenAI's new open frontier model, GPT-OSS-120B. The results were immediate: full responses in under a second.

Compare that to our previous provider: same model, 12-15 seconds per response. Cerebras wasn't just faster — it was transformative.

Real-World Impact: Real-Time Agents

Our platform generates AI personas for user research. With Cerebras, we can chain multiple reasoning steps instantly.

Users don't just get faster single responses. They get entire workflows completed in real-time. Agents can complete more tasks, deeper workflows, zero wait time.

The Trade-Offs (There Aren't Many)

Cerebras doesn't support every model. But they cover what matters: Llama 3.1-8B, Llama 3.3-70B, Qwen 3 series, GLM-4.7, and now GPT-OSS-120B.

For our use case — persona generation with structured outputs — the supported models are more than sufficient. The speed gain outweighs any model limitations.

Cost Reality Check

Faster inference often means higher costs. But Cerebras' high throughput actually reduces cost per query at scale.

When you're processing thousands of persona generations daily, throughput matters. Cerebras handles heavy demand without performance degradation.

Final Verdict

If you need instant AI responses — for chatbots, code generation, or autonomous agents — Cerebras is the best option we've tested.

We're not going back to GPU inference for our core generation pipeline. The user experience difference is too significant.