The World's Fastest AI Inference
Wafer-scale inference that removes network latency, delivers breakthrough token throughput, and makes frontier models feel instant.
Instead of assembling clusters of networked GPUs, Cerebras built an entire AI processor on a single silicon wafer. The result: inference speeds that fundamentally change what's possible for real-time applications and autonomous agents.
Why Speed Matters for AI
Inference speed isn't just a benchmark it's a core business constraint. Every extra millisecond compounds across multi-step workflows, agent reasoning chains, and real-time interactions.
Slow inference forces teams to either trim down their applications or accept skyrocketing infrastructure costs. Cerebras solves this by removing the network latency bottleneck entirely.
Wafer-Scale Advantage
By placing the entire AI compute fabric on a single wafer-scale engine (WSE), Cerebras avoids the inter-GPU communication delays that limit distributed systems. Tokens flow across one coherent chip instead of hopping between many.
Performance That Dominates
- 3,000 tokens/second on frontier models (GPT-OSS-120B).
- 2,100 tokens/second on Llama 3.1 70B.
- 969 tokens/second on Llama 3.1 405B the largest open model.
- Up to 70× faster than leading GPU solutions.
- Up to 20× faster than NVIDIA's most optimized cloud stacks.
Cerebras running Llama 3.1 70B can outperform GPU clusters running Llama 3.1 3B—delivering a 184× performance advantage on the larger, more capable model.
Key Capabilities
Cerebras Inference turns frontier models into real-time systems for agents, applications, and users.
Ultra-Low Latency Architecture
Wafer-scale integration removes inter-processor communication latency. Full turns (request → inference → response) complete in ~0.4 seconds vs. 1.1–4.2 seconds on GPU-based stacks.
Frontier Model Support
Run full-parameter frontier models like GPT-OSS-120B, Llama 4, Qwen 3, and GLM 4.6 without quantization. No trade-off between speed and model quality.
Long-Context Document Processing
Process 50K+ token documents entire codebases, legal contracts, multi-book corpora with sub-second responses, enabling true whole-context understanding.
Real-Time Autonomous Agents
Multi-step reasoning, tool use, and decision-making chains that previously took seconds or minutes now complete in under a second, making real-time agents truly viable.
Full-Context Code Generation
Analyze and transform entire codebases in a single context window. Generate features, debug, and refactor with full architectural awareness up to 20× faster than industry-standard coding stacks.
OpenAI-Compatible API
Drop-in replacement for existing LLM APIs. Migrate to Cerebras by changing endpoints and keys in LangChain, LlamaIndex, and other frameworks—no full rewrite required.
Real-World Impact: What Becomes Possible
Voice AI at Human Speed
LiveKit, powering systems like ChatGPT's voice interaction, achieves human-level conversational latency because the entire pipeline (STT → LLM → TTS) runs faster than just inference on competing GPU infrastructure.
Autonomous Research Agents
GSK builds drug discovery agents that reason over scientific literature, molecular databases, and experiment designs. Workflows that took human teams days now execute in minutes.
Instant Enterprise Search
Platforms like Notion AI deliver real-time, contextual document search over massive knowledge bases, returning high-quality answers instantly instead of relying on batch indexing and offline jobs.
Full-Context Code Generation
Engineering teams can run whole-repo analysis and refactors inside a single Cerebras context window, enabling faster feature delivery and safer large-scale code changes.
Autonomous Multi-Agent Systems
Teams of agents can coordinate, reason, and act in real time without being bottlenecked by slow inference—unlocking use cases that were previously impractical.
Why GenAI Protos Partners with Cerebras
Our clients expect AI systems that feel instant whether they power real-time voice experiences, autonomous agents, or complex reasoning workflows. Latency is not acceptable.
Cerebras Inference lets us deliver applications that match the speed of human cognition while operating with frontier-model intelligence.
By building on Cerebras, we offer architectures that are both faster and more economical than traditional GPU-based stacks at scale.
Strategic Advantages for Our Clients
- 1. Speed-Enabled ArchitectureApplications that were previously impossible due to latency constraints—real-time agents, voice AI, multi-step reasoning—become viable and performant at production scale.
- 2. Frontier Model AccessRun the most capable open models without degrading them through aggressive quantization. Clients get maximum model quality and maximum speed.
- 3. Cost Efficiency at ScaleWafer-scale efficiency reduces per-token cost while preserving performance. As usage grows, Cerebras becomes more economical than GPU alternatives for sustained workloads.
For enterprises building mission-critical AI, Cerebras Inference removes the latency bottleneck that limits what AI can accomplish.
Infrastructure & Pricing
Cerebras is deploying a global inference footprint targeting 40+ million tokens per second by the end of 2025.
Transparent cloud pricing starts around $0.00001 per token with a free tier of 1M tokens/day for development, making it practical to prototype high-speed applications before committing to scale.
For enterprises needing complete control and data sovereignty, on-premises CS-3 systems bring wafer-scale inference directly into their own data centers.
Cost & Deployment Options
Cloud Inference
- Pay-per-token pricing, starting at ~$0.00001/token
- Free development tier (1M tokens/day)
- Elastic capacity for bursty workloads
On-Prem CS-3
- Full control over data and workloads
- Predictable cost profile for high-volume use
- Ideal for regulated and latency-sensitive industries
Ready to build ultra-fast AI with Cerebras Inference?
Partner with our team to design, benchmark, and deploy Cerebras-powered systems for your most latency-sensitive workloads.
Cerebras Inference FAQ
Answers to common questions about Cerebras wafer-scale inference and how GenAI Protos uses it in production.