Ricursive Intelligence

Ripple 65-B Preview from @RicursiveAI is on Ollama. Built for agentic coding, with up to 1M tokens of context. Run it locally with full zero data retention.

> we developed and trained Ripple-65B Preview for only $32M, which is one-tenth the cost of the previous open-weight SOTA on agentic coding¹ this is like the avengers of open model SOTA ¹ "Note that $32M is our full R&D cost starting from scratch, including pretraining ladder development, data mixing, evals, debugging, synthetic data generation, posttraining recipe development, and the final hero pretraining and posttraining run."

Congrats to @RicursiveAI on Ripple-65B Preview, their first open model 🎉 It was trained on NVIDIA B300 GPUs & sharpened with open NVIDIA Nemotron post-training datasets. Download the NVFP4 checkpoint from Hugging Face and run locally on DGX Spark 👉 huggingface.co/ricursiveAI/R…

Ok, *this is impressive*: Ricursive Intelligence just released Ripple-65B Preview, an open-weight AI model that it says outperforms much larger US models on a key agentic coding benchmark. And even runs locally on my DGX Spark. Ripple scored 80.5% on Terminal-Bench 2.1, narrowly ahead of Reflection’s Beam-501B at 80.1%, according to the companies’ reported results! The size comparison comes with a detail: Ripple uses all 65B parameters for each task, while Beam activates 23B of its 501B parameters. Ricursive says Ripple was trained on 12 trillion tokens with a $32 million compute budget - from scratch. The model supports up to 1 million tokens of context and is available under Apache 2.0.

@StanfordAILab faculty building incredible open models out in the wild! Congratulations to the @RicursiveAI team!