What happened
Ricursive Intelligence, an AI lab focused on chip design, released its first large language model, Ripple-65B Preview, on Hugging Face. The model is a dense transformer with 65 billion active parameters, trained on 12 trillion tokens, and licensed under Apache 2.0. The company reports a score of 80.5 on Terminal-Bench 2.1, surpassing larger mixture-of-experts models, though these results are self-reported and not yet independently verified.
Ricursive Intelligence, founded by Anna Goldie and Azalia Mirhoseini, released Ripple-65B Preview on November 4. The model is a dense transformer with 65 billion parameters, all active per token, trained on 12 trillion tokens of text and code. It features a 1-million token context window and is licensed under Apache 2.0, allowing commercial use and modification.
The company reports a score of 80.5 on Terminal-Bench 2.1, an agentic coding benchmark, which it claims is the highest among U.S. open- models. This score surpasses larger MoE models like Reflection Beam (80.1), Muse Spark Open (76.4), and Nemotron 3 Ultra (56.4). However, these results are self-reported and lack independent verification.
Ricursive states that the model was developed for $32 million, a tenth of the cost of previous top open models for agentic coding. The training process achieved 40% MFU using simple sharding, without custom kernels or quantization. The model is available in BF16 and NVFP4 formats, with community GGUF quantizations also available.
The model's architecture includes sliding-window attention and global attention layers, designed to reduce training costs. The pretraining corpus is text-only, mostly English and code, with no synthetic data in the base checkpoint. Post-training was limited and focused on agentic coding, which may explain its lower performance in general knowledge benchmarks like MMLU.
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Why it matters
The release is significant because it challenges the industry trend toward mixture-of-experts (MoE) architectures by demonstrating that a dense model can achieve competitive agentic coding performance with lower training infrastructure complexity. Ricursive claims the model was developed for $32 million, a fraction of the cost of comparable open models, and that it achieves 40% Model FLOPs Utilization (MFU) without custom kernels. This suggests a more efficient path for smaller labs to train high-performance models. However, the model's performance in general knowledge and other coding benchmarks trails larger rivals, and its practical utility on consumer hardware remains unverified due to high memory requirements.
Ripple-65B Preview's dense architecture offers a contrast to the prevailing MoE trend, potentially simplifying training infrastructure and reducing costs. Ricursive's claim of 40% MFU without custom kernels suggests a more efficient training process, which could be valuable for smaller labs with limited resources.
The model's strong performance on agentic coding tasks, if verified, could make it a viable option for developers looking for open- models for terminal-based automation. However, its high memory requirements (130 GB in BF16) limit its use on consumer hardware, with local deployment likely requiring aggressive quantization.
The release highlights the ongoing competition among open- models, with Ricursive positioning itself as a cost-effective alternative to larger, more complex MoE models. The model's Apache 2.0 license makes it attractive for commercial applications, though its performance in non-agentic tasks remains a limitation.
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What to watch next
Independent benchmarks of Ripple-65B Preview on Terminal-Bench 2.1 and other agentic coding tasks will be crucial to validate Ricursive's claims. Community feedback on the model's performance on local hardware, particularly regarding memory bandwidth and quantization efficiency, will determine its practical adoption. Additionally, the release of a final, fully post-trained version of the model is expected, which may improve performance in non-agentic tasks.
Independent benchmarks of Ripple-65B Preview on Terminal-Bench 2.1 and other agentic coding tasks will be essential to confirm Ricursive's claims. Community members on r/LocalLLaMA are already discussing the model, and independent tests are expected to provide a more accurate picture of its performance.
The practical usability of the model on local hardware will be a key factor in its adoption. Users will need to assess the trade-offs between quantization levels and performance, particularly on machines with limited memory bandwidth.
The release of a final version of Ripple-65B, with more extensive post-training, could improve its performance in general knowledge and other coding benchmarks. Ricursive has not set a date for this release, but it is likely to be a focus for the company in the coming months.