Cerebras-GPT

Stable LM: Stability AI Language Models. The release of Stable LM builds on our experience in open-sourcing earlier language models with EleutherAI, a nonprofit research hub. These language models include GPT-J, GPT-NeoX, and the Pythia suite, which were trained on The Pile open-source dataset. Many recent open-source language models continue to build on these efforts, including Cerebras-GPT and Dolly-2. Stable LM is trained on a new experimental dataset built on The Pile, but three times larger with 1.5 trillion tokens of content. We will release details on the dataset in due course. The richness of this dataset gives Stable LM surprisingly high performance in conversational and coding tasks, despite its small size of 3 to 7 billion parameters (by comparison, GPT-3 has 175 billion parameters). Stable LM 3B is a compact language model designed to operate on portable digital devices like handhelds and laptops, and we’re excited about its capabilities and portability.

GPT-5.3-Codex-Spark is an ultra-fast coding model designed for real-time collaboration inside Codex. Built as a smaller version of GPT-5.3-Codex, it delivers over 1000 tokens per second when served on low-latency Cerebras hardware. The model is optimized for interactive coding tasks, enabling developers to make targeted edits and see results almost instantly. With a 128k context window, Codex-Spark supports substantial project context while maintaining speed. It focuses on lightweight, precise edits and does not automatically run tests unless prompted. Infrastructure upgrades such as persistent WebSocket connections significantly reduce latency across the full request-response pipeline. Released as a research preview for ChatGPT Pro users, Codex-Spark marks the first milestone in OpenAI’s partnership with Cerebras.