Cerebras
Wafer-scale AI chips — 4,500 tokens/sec, the fastest inference on earth
Cerebras uses wafer-scale silicon (the CS-3 chip covers an entire silicon wafer) to deliver extraordinary inference throughput. Llama 3.1 8B runs at 4,500+ tokens/second — roughly 10× faster than GPU-based providers. This makes Cerebras uniquely suited for real-time applications, voice AI, and interactive coding assistants.
Cerebras delivers 4,500+ tokens/sec on Llama 3.1 8B — 10× faster than any GPU provider, enabling genuinely real-time AI applications.
- 4,500+ tokens/sec on Llama 3.1 8B — fastest inference available
- Sub-50ms time-to-first-token for real-time applications
- Wafer-scale chip architecture eliminates GPU memory bottlenecks
- Competitive pricing for the throughput delivered
- OpenAI-compatible API
- Very limited model selection — only a few Llama variants
- No vision or multimodal support
- No fine-tuning capability
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Full comparison tableCerebras LLM pricing overview
Cerebras is an inference API provider that hosts open-weight and third-party large language models. Cerebras uses wafer-scale silicon (the CS-3 chip covers an entire silicon wafer) to deliver extraordinary inference throughput. Llama 3.1 8B runs at 4,500+ tokens/second — roughly 10× faster than GPU-based providers. This makes Cerebras uniquely suited for real-time applications, voice AI, and interactive coding assistants. Common use cases include Voice AI, Real-time chat, Speed, Interactive coding. Headquartered in Sunnyvale, CA, founded 2016. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Cerebras vs other LLM providers
Cerebras competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. Cerebras delivers 4,500+ tokens/sec on Llama 3.1 8B — 10× faster than any GPU provider, enabling genuinely real-time AI applications. Use the LLM provider comparison tool to see Cerebras token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Cerebras?
Cerebras's key strengths are: 4,500+ tokens/sec on Llama 3.1 8B — fastest inference available; Sub-50ms time-to-first-token for real-time applications; Wafer-scale chip architecture eliminates GPU memory bottlenecks. Limitations to consider: Very limited model selection — only a few Llama variants; No vision or multimodal support. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Cerebras token pricing
Cerebras charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. Prices shown are sourced from Cerebras's public pricing page and updated daily. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Cerebras context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Cerebras makes sense
Managed inference APIs like Cerebras eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Cerebras API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Cerebras token price data is available in the LLM price history charts.