🇺🇸 United States · Cerebras — Ultra-Fast AI Inference Hardware and Cloud
Status: 🟩 COMPLETE 🟦 LIVING Section: 15 — Broader Tech Bonus
| Vendor | Cerebras Systems |
| Country/origin | 🇺🇸 United States (Sunnyvale, California) |
| Recommended for AUS? | ✅ Yes — US-based; standard enterprise privacy |
| Privacy summary | API service; standard enterprise data handling; inputs not used for model training; US data centres |
| Free tier | Yes — Cerebras API free tier with rate limits |
| Paid tiers | Pay-per-token API pricing; enterprise contracts |
| First released | Cerebras Systems founded 2016; Wafer Scale Engine 2019; Cerebras Inference API launched 2024 |
| Last reviewed | June 2026 |
| Official site | https://cerebras.ai |
What it is
Cerebras is a US semiconductor and AI company famous for building the world’s largest computer chip — the Wafer Scale Engine (WSE) — specifically designed to run AI workloads at extraordinary speeds.
To understand why this matters: most AI processing uses GPUs (graphics cards, originally designed for video games but repurposed for AI). Cerebras took a completely different approach — they designed a chip that is an entire silicon wafer (a single, dinner-plate-sized chip), rather than cutting wafers into many smaller chips. The WSE-3 (2024) has 4 trillion transistors and 44 GB of on-chip memory. It’s physically enormous compared to any other chip.
The result: Cerebras can run AI model inference (generating responses) at speeds that are sometimes 20–40× faster than GPU-based systems, particularly for large language models where the bottleneck is memory bandwidth rather than raw compute.
Cerebras Cloud / API: In 2024, Cerebras made their hardware available via a cloud API — so you can access the raw speed of their WSE hardware without buying the physical hardware. They run open-weights models (like Llama) on their WSE hardware and offer an API-compatible service.
What you’d use it for
- Applications requiring very low latency (fast response times): Chatbots that need to feel instant; real-time voice AI applications; interactive coding assistants
- High-throughput AI workloads: Processing large batches of text very fast
- Research: Testing how AI applications perform at much higher speeds
- Comparing inference providers: Benchmarking whether your application benefits from faster generation
How to access from Australia
- Go to https://inference.cerebras.ai
- Sign up for an account
- Free tier provides access with rate limits — good for testing
- API endpoint is compatible with OpenAI API format, so existing code that uses OpenAI can often be pointed at Cerebras with minimal changes
- Select a model (Llama-3.1-8B, Llama-3.1-70B, Llama-3.3-70B available as of 2025–2026)
Speed benchmarks (approximate, mid-2026)
| Provider | Model | Tokens per second |
|---|---|---|
| Cerebras | Llama-3.3-70B | ~2,100 tok/s |
| Groq | Llama-3.1-70B | ~800 tok/s |
| Together AI | Llama-3.1-70B | ~200–500 tok/s |
| OpenAI | GPT-4o | ~100–200 tok/s |
“Tokens per second” measures how fast the AI generates text. Higher = faster, more responsive.
What it costs
Cerebras pricing is competitive, particularly given the speed advantage. As of 2025–2026:
- Llama-3.1-8B: ~0.10 per million output tokens
- Llama-3.1-70B: ~0.60 per million output
- Free tier: limited requests per minute for development and testing
Pricing is similar to other inference providers for the same models, but you get dramatically faster responses.
How it compares to alternatives
| Provider | Speed | Best open model | Best for |
|---|---|---|---|
| Cerebras | Fastest | Llama-3.x | Speed-critical applications |
| Groq | Very fast | Llama-3.x, Mixtral | Speed + model variety |
| Together AI | Fast | Wide variety | Broad model selection |
| Fireworks AI | Fast | Llama, Mistral | Production reliability |
| Replicate | Moderate | Anything open-weights | Ease of use |
| OpenAI | Moderate | GPT-4o | Best closed-source quality |
Cerebras and Groq compete directly for the “fastest inference” market. Cerebras has the edge in raw speed for large models; Groq has wider model availability and a more mature platform.
The Wafer Scale Engine — a plain English explanation
A standard GPU chip is about the size of your thumbnail (when removed from its packaging). Manufacturers cut a wafer of silicon into hundreds of these chips.
Cerebras instead uses the entire wafer as one single chip. Imagine you’re making cookies: standard GPUs cut the cookie dough into 200 small cookies. Cerebras bakes one enormous single cookie the size of the baking tray.
The advantage: communication between parts of the chip happens at silicon speed (nearly instant), rather than through slow external connections. For AI, where enormous amounts of data need to flow through the “memory” of the chip, this eliminates a major bottleneck.
The disadvantage: manufacturing a perfect dinner-plate-sized chip is extremely difficult. Any defect in any part ruins the chip. Cerebras has engineering solutions to manage this, but it’s why no one else has attempted this approach.
Gotchas
- Not all tasks benefit equally from speed. If your application spends most time on user input or database queries (not AI generation), the speed advantage of Cerebras won’t be as impactful.
- Limited model selection. Cerebras runs specific open-weights models. You can’t access GPT-4o, Claude, or Gemini through Cerebras — only the models they’ve adapted for their hardware.
- Australian latency: Cerebras servers are in the US. For Australian deployments, network latency adds some delay. The generation speed is still much faster, but the round-trip time to the US is a factor.
- Enterprise features still developing. For large-scale production deployments needing advanced security, data residency, or SLAs, AWS/Azure/GCP-hosted inference may have more mature enterprise tooling.
- Rate limits on free tier. The free tier is useful for development but rate-limited. Production workloads require a paid arrangement.
See also
- groq — main competitor; fast inference; wider model selection
- together-ai — alternative fast inference provider
- fireworks-ai — production-grade fast inference
- nvidia-ai — GPU-based AI hardware (Cerebras’s main alternative approach)
- llama — the models Cerebras runs
Sources
- Cerebras Systems official documentation: cerebras.ai
- Cerebras Inference API documentation: inference.cerebras.ai
- Cerebras WSE-3 announcement (2024)
- Independent benchmark comparisons: ArtificialAnalysis.ai (2024–2026)
- IEEE Spectrum coverage of Cerebras chip architecture
- TechCrunch funding and product announcement coverage (2022–2024)