NVIDIA Alpamayo 2 Super vs. Inkling-Small

NVIDIA Alpamayo 2 Super emerges as an innovative open model specifically designed for robotaxis and autonomous vehicles, capable of navigating unique and complex driving situations while producing decisions that developers can thoroughly analyze, validate, and trust. Built on the principles of NVIDIA Cosmos 3 Super Reasoner and further enhanced through reinforcement learning techniques, it balances commercial viability with the capability to manage various tasks associated with autonomous driving. The model conducts an in-depth analysis of full-surround camera feeds, elegantly merging viewpoints from the front, sides, and rear to competently tackle lane changes, merges, unprotected turns, and intricate intersections. For every driving situation it encounters, it is equipped to generate a planned trajectory for the vehicle, a chain-of-causation that clarifies the decision-making pathway, meta-actions like yielding or stopping, and reasoning auto-labels intended for both training and validation, alongside visual question-answering outputs that are grounded in specific regions of the images. These interconnected outputs not only enhance the relationship between the model’s observations and the actions it executes but also significantly improve transparency in the autonomous decision-making process. Furthermore, this sophisticated functionality aids developers in fine-tuning and boosting the model's performance for practical applications in the real world, ensuring that it meets the rigorous demands of autonomous navigation. Thus, it represents a significant advancement in the field of autonomous driving technology.

Inkling-Small is an efficient multimodal AI model built to deliver strong reasoning and coding performance at a fraction of Inkling’s size. It is a Mixture-of-Experts transformer with 276 billion total parameters and 12 billion active parameters. The model was trained on NVIDIA GB300 NVL72 systems and is designed to combine high capability with more efficient inference. Inkling-Small supports native reasoning across text, images, and audio, allowing it to work across multimodal tasks without relying on separate encoders. Its context window supports up to one million tokens, making it useful for long-form reasoning, large-scale code understanding, document analysis, and agentic workflows. Users can adjust reasoning effort from minimal to extra high depending on whether they need faster responses or deeper computation. The model’s training process includes improved pre-training data, post-training with on-policy distillation from Inkling, and extended agentic coding reinforcement learning. These techniques helped Inkling-Small outperform its larger counterpart on reasoning and coding benchmarks. The model performs well in coding and tool-use harnesses and exceeds 80% on SWE-bench Verified. Its encoder-free architecture processes audio as dMel spectrograms and images as 40-by-40-pixel patches alongside text tokens. By combining efficient MoE design, one-million-token context, adjustable reasoning effort, multimodal processing, coding strength, and tool-use performance, Inkling-Small is designed for developers and teams that need capable AI with lower active compute requirements.