Cerebras Captures Latency Sensitive Workloads
OpenAI's side chip
Cerebras is winning where waiting even a second breaks the product. In coding agents, voice, and multi step agent loops, the bottleneck is not just raw compute, it is how fast each next token appears. Cerebras is built to keep compute, memory, and bandwidth on one giant chip, while Nvidia systems lean on GPU clusters and software layers like batching and cache tricks that are great for throughput but less ideal when every round trip matters.
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The clearest proof is OpenAI. It added 750 MW of Cerebras capacity specifically for ultra low latency inference, then launched GPT-5.3-Codex-Spark on Cerebras as a latency first tier delivering more than 1,000 tokens per second for real time coding.
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This matters most in products that chain many model calls together. A coding agent that reads files, writes code, runs tests, and retries can make dozens of inference calls. Cutting delay on each step turns a slow minute long workflow into something that feels interactive.
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Nvidia is still strongest for general purpose AI because CUDA, TensorRT-LLM, and GPU fleets are the default stack. But Nvidia’s own inference software emphasizes in flight batching, paged KV caching, and other optimizations that trade around the limits of shared GPU systems, while Cerebras is selling a hardware first answer for the narrow slice of work where jitter and delay are the whole problem.
The next step is a split inference market. Nvidia will remain the broad platform for most training and serving, while Cerebras, Groq, and similar ASIC players take the premium tier for real time workloads where speed changes user behavior, unlocks more agent steps, and lets model providers charge for a better product instead of just more tokens.