Cerebras

Company Overview

Cerebras takes the most radical approach in AI hardware: building the largest chip ever made.^[1] The Wafer-Scale Engine (WSE) uses an entire silicon wafer as a single processor, containing 4 trillion transistors — approximately 56x larger than the largest NVIDIA GPU.

With major funding, government and national lab customers, and announced IPO plans, Cerebras represents the high end of the custom silicon bet: that purpose-built hardware will outperform general-purpose GPUs for AI workloads.

What It Does

• Cerebras Inference — Cloud API for fast LLM inference on WSE hardware^[2]
• Training systems — CS-3 systems for large-scale model training
• Enterprise deployments — On-premise WSE systems for organizations
• Research partnerships — Collaborations with national labs and universities

How It Works

The Wafer-Scale Engine eliminates the interconnect bottleneck that limits GPU clusters. Instead of networking thousands of small chips together, Cerebras puts everything on one massive chip:

• 900,000 AI cores on a single wafer
• 44GB on-chip SRAM — no external memory bottleneck
• Wafer-scale interconnect — all cores communicate at silicon speed
• MemoryX — External memory system for models larger than on-chip capacity

For inference, Cerebras offers an API similar to other providers. For training, customers deploy CS-3 systems (each containing one WSE-3).

Pricing

• Inference API — Per-token pricing, competitive with GPU platforms
• Free tier — Available for experimentation
• Enterprise systems — Custom pricing for CS-3 hardware deployments
• Training — Custom pricing based on scale and duration

Strengths

• Fastest single-model throughput — WSE architecture excels at individual model speed
• No interconnect bottleneck — Single chip eliminates multi-GPU communication overhead
• On-chip memory — 44GB SRAM avoids HBM bandwidth limits
• Enterprise credibility — Government and national lab customers
• IPO trajectory — Signals financial maturity and long-term viability
• Training + inference — Full-stack custom silicon platform

Weaknesses / Risks

• Limited model ecosystem — Only supported models available on inference API
• No fine-tuning API — Less accessible than GPU platforms for customization
• Hardware cost — WSE systems are expensive; cloud API offsets this
• Supply constraints — Wafer-scale manufacturing is complex and limited
• Enterprise-only on-prem — Not accessible for smaller teams (cloud API mitigates)
• IPO uncertainty — Market conditions could delay or complicate public listing

Competitive Landscape

vs. Groq: Both custom silicon. Groq focuses on deterministic latency for individual requests; Cerebras on maximum throughput via wafer-scale.

vs. NVIDIA/GPU platforms: GPUs offer more model flexibility and ecosystem. Cerebras wins on raw performance for supported models.

vs. SambaNova: Both custom silicon with enterprise focus. Cerebras has the more radical hardware approach; SambaNova focuses on reconfigurability.

vs. Modal/Baseten: GPU platforms offer more flexibility. Cerebras wins on speed for supported workloads.

Ideal User

• Enterprise teams needing maximum inference throughput
• Research organizations and national labs with large-scale AI workloads
• Organizations willing to invest in custom hardware for performance
• Teams running supported models where speed is the primary metric

Bottom Line

Cerebras represents the boldest bet in AI hardware — that a single massive chip beats a cluster of smaller ones. The Wafer-Scale Engine delivers on performance for supported models, and the inference API makes it accessible beyond enterprise hardware buyers. The risk is ecosystem breadth: GPU platforms support more models and use cases. For organizations where raw speed on supported models is paramount, Cerebras is a compelling option. IPO plans suggest the company believes the market is ready for custom silicon at scale.

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