Cerebras vs Etched: Which AI Tool Is Better in 2026?

Cerebras's WSE-3 is the largest AI chip ever built, measuring 46,225 mm² and containing 4 trillion transistors with 900,000 AI-optimized cores delivering 125 petaflops of compute, while Etched builds Sohu—a transformer-specific ASIC chip betting that fixed-function silicon beats general-purpose GPUs for transformer inference.

These represent fundamentally opposed architectural philosophies in the race to dethrone GPU dominance. Cerebras became a public company on May 14, 2026, via the largest U.S. technology initial public offering since 2019, signaling investor confidence in wafer-scale deployment at scale.

Etched has raised close to one billion in total funding at a five billion valuation but remains private and in early customer ramp, with the chip not publicly available for purchase or rental as of April 2026.

Cerebras targets broad inference workloads—language models, reasoning tasks, scientific computing—across its cloud platform and sold systems, leveraging routinely ranking among the fastest inference providers in the world with over 2,200 tokens per second when running GPT-OSS 120B High.

Etched's Sohu is narrower by design: one 8-chip Sohu server delivers 500,000 tokens/sec on Llama 70B, a jaw-dropping per-chip figure if true, but Sohu cannot run CNNs, RNNs, LSTMs, deep learning recommendation models, or AlphaFold 2—limiting it to transformer inference only.

Sohu claims 90%+ FLOPS use where H100 transformers typically hit 30-40% of peak FLOPS; by eliminating instruction overhead, Sohu can theoretically deliver 2-3x more useful compute from the same transistors.

This is the core tension: Cerebras trades specialization for flexibility and proven production scale across multiple model families; Etched bets everything on transformers remaining the dominant architecture long enough to amortize its bet.

For organizations running diverse workloads and needing immediate deployment, Cerebras is operationally ready today.

For enterprises serving pure transformer-based services at hyperscale with tolerance for single-architecture lock-in and longer timelines, Sohu's theoretical cost-per-token advantage could prove significant—if it ships, works at claimed performance, and transformers do not cede ground to emerging architectures.