Cerebras Cloud

Cerebras Cloud provides inference acceleration using wafer-scale engines that keep entire models on a single chip, enabling record-breaking speeds for dense models like gpt-oss-120B (2,700 tokens/s) and Qwen 235B (18× faster than leading GPUs). It removes the traditional trade-off between model size and speed.

Benefits include consistent high throughput across single- and multi-user scenarios, sub-second response times for giant models, and up to 10× better price-performance than GPU counterparts for specific workloads.

Drawbacks: the hardware is large and purpose-built, making it difficult to integrate with existing GPU clusters and limiting ecosystem flexibility. For very large mixture-of-experts (MoE) models, the advantage shrinks if experts cannot fit in on-chip SRAM. Performance claims depend on specific benchmarks and configurations.

Cost and deployment: no public pricing is available in the evidence; Cerebras sells full systems rather than standalone chips, suggesting a premium deployment cost. It suits frontier AI labs needing extreme latency and throughput, but it may not fit teams that require hybrid GPU environments, broad software support, or tight budget control.