HomeOpenAI’s Codex New Version Powered by Dedicated Chip
On Thursday, OpenAI unveiled a lightweight version of its agentic coding tool Codex, whose latest model was released earlier this month. GPT-5.3-Codex-Spark is described as a “smaller version” optimized for faster inference. To support that speed, OpenAI is using a dedicated chip from its hardware partner Cerebras, signaling a deeper integration within its physical infrastructure.
The partnership between Cerebras and OpenAI was announced last month, with OpenAI revealing a multi-year agreement valued at over $10 billion. “Bringing Cerebras into our compute mix is about making our AI respond much faster,” the company stated. Now, OpenAI calls Spark the “first milestone” in that collaboration.
Spark, built for real-time collaboration and rapid iteration, runs on Cerebras’ Wafer Scale Engine 3. The WSE-3 is Cerebras’ third-generation waferscale megachip, featuring 4 trillion transistors. OpenAI describes this lightweight tool as a “daily productivity driver” for rapid prototyping, as opposed to the heavier, longer tasks handled by the original 5.3. Spark is currently available in a research preview for ChatGPT Pro users through the Codex app.
In a tweet ahead of the announcement, CEO Sam Altman hinted at the new model. “We have a special launch for Codex users on the Pro plan later today,” Altman wrote. “It sparks joy for me.”
In its official statement, OpenAI emphasized that Spark is built for the lowest possible latency on Codex. “Codex-Spark is the first step toward a Codex with two complementary modes: real-time collaboration for rapid iteration, and long-running tasks for deeper reasoning and execution,” OpenAI said. The company added that Cerebras’ chips are particularly effective for “workflows that demand extremely low latency.”
Cerebras has been around for over a decade, but in the AI era it has gained a more prominent role in tech. Just last week, the company announced it raised $1 billion in new capital at a $23 billion valuation. Cerebras has previously stated its intention to pursue an IPO.
“What excites us most about GPT-5.3-Codex-Spark is partnering with OpenAI and the developer community to explore what fast inference enables — new interaction patterns, new use cases, and a fundamentally different model experience,” said Sean Lie, CTO and co-founder of Cerebras. “This preview is just the beginning.”
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On Thursday, OpenAI unveiled a lightweight version of its agentic coding tool Codex, whose latest model was released earlier this month. GPT-5.3-Codex-Spark is described as a “smaller version” optimized for faster inference. To support that speed, OpenAI is using a dedicated chip from its hardware partner Cerebras, signaling a deeper integration within its physical infrastructure.
The partnership between Cerebras and OpenAI was announced last month, with OpenAI revealing a multi-year agreement valued at over $10 billion. “Bringing Cerebras into our compute mix is about making our AI respond much faster,” the company stated. Now, OpenAI calls Spark the “first milestone” in that collaboration.
Spark, built for real-time collaboration and rapid iteration, runs on Cerebras’ Wafer Scale Engine 3. The WSE-3 is Cerebras’ third-generation waferscale megachip, featuring 4 trillion transistors. OpenAI describes this lightweight tool as a “daily productivity driver” for rapid prototyping, as opposed to the heavier, longer tasks handled by the original 5.3. Spark is currently available in a research preview for ChatGPT Pro users through the Codex app.
In a tweet ahead of the announcement, CEO Sam Altman hinted at the new model. “We have a special launch for Codex users on the Pro plan later today,” Altman wrote. “It sparks joy for me.”
In its official statement, OpenAI emphasized that Spark is built for the lowest possible latency on Codex. “Codex-Spark is the first step toward a Codex with two complementary modes: real-time collaboration for rapid iteration, and long-running tasks for deeper reasoning and execution,” OpenAI said. The company added that Cerebras’ chips are particularly effective for “workflows that demand extremely low latency.”
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