Cerebras vs. NVIDIA H200: when to use which

We're one of the few clouds that runs both NVIDIA and Cerebras at production scale. After six months of customer workloads, here's our honest take on when to use which — and when to use both.

Cerebras in one sentence

Cerebras is the lowest-latency inference platform commercially available, by a wide margin. The CS-3 wafer-scale engine keeps the entire model in on-chip SRAM, so you never pay the memory-bandwidth tax that bottlenecks GPUs.

NVIDIA H200 in one sentence

NVIDIA H200 is the most versatile production accelerator available. It does training, fine-tuning, batch inference, and real-time inference — all well, none optimally.

The decision tree

If your workload fits any of these patterns, use Cerebras:

• →Real-time or interactive inference with sub-50ms p99 latency requirements.
• →Agentic workloads where every millisecond compounds.
• →Long-context inference (32k+ tokens) where prefill time dominates.

If your workload fits any of these, use NVIDIA H200:

• →Training or continued pretraining (Cerebras is not optimized for this yet).
• →Fine-tuning (LoRA, full fine-tune) — H200 has the mature tooling.
• →Multi-model serving where you want flexibility to swap models quickly.
• →Batch inference where latency doesn't matter and you're optimizing for $/token.

The most common pattern

Most production inference teams we talk to end up running both: NVIDIA H200 for the bulk of their traffic (cheaper, more flexible) and Cerebras CS-3 for the latency-critical subset (interactive agents, real-time search, voice). AURA routes the request to the right backend automatically.