The GPU was not designed for AI inference. It was designed for rendering polygons, repurposed for training neural networks, and adapted — somewhat awkwardly — for serving them. The result is a hardware stack with deep inefficiencies for the inference workload: data must travel between stacked memory chips, computation is distributed across thousands of small cores with coordination overhead, and thermal constraints limit sustained throughput.
Cerebras took a different path. The CS-3 Wafer-Scale Engine places the entire computation on a single silicon wafer — roughly the size of an iPad. No inter-chip communication. No memory hierarchy. No coordination overhead between chiplets. The result is inference throughput that changes what is architecturally possible for AI agents running at scale.
AACFlow supports Cerebras as a provider, enabling any workflow block to route to Cerebras for the inference steps where hardware-level speed matters.
The Wafer-Scale Architecture
A standard GPU die is around 800mm². A Cerebras CS-3 wafer is 46,225mm² — 57 times larger. That single silicon substrate contains 900,000 AI-optimized cores and 44GB of on-chip SRAM.
The critical difference is memory architecture. GPU inference requires constant data movement between high-bandwidth memory (HBM) chips and the compute dies. This movement is the bandwidth wall — the fundamental limit on inference throughput for GPU-based systems. On the CS-3, model weights and activations live in on-chip SRAM, directly accessible to compute cores without external memory traversal.
For models that fit in that 44GB footprint — which includes the distilled and optimized versions of Llama 4 Scout and Maverick — the throughput multiplier over GPU is significant: roughly 20–100x faster for the same model size, depending on batch configuration.