Inkling vs. Activeloop

Inkling is an open-weights multimodal AI model from Thinking Machines built to support customization, agentic workflows, coding, reasoning, vision, audio, and enterprise AI use cases. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, 256 routed experts per MoE layer, and six routed experts active per token. It supports context windows up to 1 million tokens and was pretrained on 45 trillion tokens across text, images, audio, and video. Inkling is designed as a broad foundation model rather than a narrowly optimized benchmark model, giving it balanced capabilities across reasoning, coding, factuality, instruction following, vision, audio, tool use, and safety. Its controllable thinking effort lets developers adjust how much computation and generated reasoning the model uses, helping teams balance quality, latency, and cost for different production needs. The model can run agentic coding tasks, use tools, create web apps, generate polished multi-page artifacts, reason over long contexts, and work through iterative refinement loops. For multimodal tasks, Inkling can process images, answer questions about visual content, transcribe and reason over audio, follow spoken instructions, and combine visual reasoning with code-based tools such as Python. Thinking Machines trained Inkling for calibration, instruction following, factual reliability, refusal behavior, and safety across multiple modalities, including evaluations for dangerous capabilities and human-AI threat vectors. Inkling is available on Tinker for fine-tuning, with 64K and 256K context options, an Inkling Playground for testing, cookbook recipes, and support for multimodal post-training workflows. Its full weights are available on Hugging Face, and deployment support is available through APIs and infrastructure partners such as TogetherAI, Fireworks, Modal, Databricks, Baseten, SGLang, vLLM, llama.cpp, and transformers.

Activeloop provides a robust infrastructure tailored for continuous learning, specifically designed for teams involved in software development, agent creation, and the management of data pipelines. Central to their offerings is Deeplake, a database optimized for GPU use that caters specifically to agents, operating under the notion that if AI systems leverage GPU capabilities, the associated data must also be tailored for optimal GPU performance. By supporting the grounding, versioning, querying, and GPU integration of AI agents, Deeplake merges vector and tensor data into a single storage framework, complete with GPU streaming functionalities for fine-tuning and a serverless Postgres interface. This solution equips teams with a powerful data engine for multimodal AI, enabling them to effectively store, index, search, and stream data directly to their models and agents. Instead of perceiving AI data as a collection of disjointed files, embeddings, metadata, and traces scattered across multiple systems, Activeloop consolidates these components into an integrated infrastructure that enhances retrieval, model training, fine-tuning, and memory management for agents. Furthermore, the platform features Hivemind, which converts agent traces into shared knowledge among team members, enabling solutions developed once to be shared throughout the organization via trajectory capture, thus significantly boosting collaborative efficiency and innovation. This integration not only streamlines data management but also promotes a culture of collaboration, where teams can flourish in their AI projects and leverage combined insights for greater impact.