The Limite 1B Violetto, a 2.1 GB math model, achieved 94.01% accuracy on the AIME 2026 exam. Researchers from Paradigma trained the model from scratch and released it under Apache 2.0, along with a vLLM plugin for serving. When deployed on an RTX 4090, the model demonstrated consistent performance regardless of memory utilization, with speed remaining stable even when the GPU memory cap was significantly reduced. The key takeaway is that the memory cap in vLLM dictates the number of concurrent answers that can be processed, not the inference speed. AI
IMPACT Understanding vLLM memory utilization is crucial for efficiently deploying and scaling large language models for inference.
RANK_REASON The article details the deployment and serving of an existing model (Limite 1B Violetto) using a specific framework (vLLM), focusing on technical implementation and performance tuning rather than a new model release or research breakthrough.
- AIME 2026
- Apache Software License 2.0
- CUDA
- Hugging Face
- Limite 1B Violetto
- Paradigma
- Python
- PyTorch
- RTX 4090
- vLLM
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