Cerebras Shatters LLM Speed Record at 2,500 Tokens/Sec

Speed isn’t just a feature, it’s the architecture. Cerebras has engineered its inference stack around a single, uncompromising priority: raw token throughput without sacrificing the first-word latency that kills real-time feel. Consider these numbers: 3,115 tokens per second on gpt-oss-120B, 2,782 on the same model under lower compute, 1,669 on GLM-4.7, and 2,041 on Llama 3.3 70B, all with first-token latencies hovering around a quarter-second.

That’s not incremental improvement; that’s a categorical shift. The result? Long summaries, extraction workflows, code generation, and high-QPS production endpoints can now operate at a speed that feels less like batch processing and more like real-time thought.

This isn’t a balanced offering. It’s a speed-first manifesto, and it puts Cerebras squarely at the top of any list measuring what fast means in the LLM API race.