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neptune.ai

neptune.ai

Software Development

Palo Alto, California 37,791 followers

Experiment tracker purpose-built for foundation model training.

About us

Monitor thousands of per-layer metrics—losses, gradients, and activations—at any scale. Visualize them with no lag and no missed spikes. Drill down into logs and debug training issues fast. Keep your model training stable while reducing wasted GPU cycles.

Website
https://neptune.ai
Industry
Software Development
Company size
51-200 employees
Headquarters
Palo Alto, California
Type
Privately Held
Founded
2017
Specialties
Machine learning, Gen AI, Generative AI, LLMs, Large Language Models, LLMOps, Foundation model training, and Experiment tracking

Locations

Employees at neptune.ai

Updates

  • For large foundation models, subtle issues in just a few layers can cause silent degradation in the training process. The problem? Aggregate metrics often mask these instabilities. Without tracking layer-wise activations, gradients, and losses, you often don’t see the issues. How granular is your logging—do you monitor individual layers or only global loss? #generativeai #genai #llm

  • View organization page for neptune.ai

    37,791 followers

    Some AI questions seem impossible—until someone dares to answer. At NeurIPS 2024, we challenged Amaury Gouverneur, PhD student at Kungliga Tekniska högskolan, with some of the toughest ones, like: “What combination of existing tech plus new developments will it take for us to run billion-parameter architectures on edge devices?” Watch to hear his perspective. — (Link to the full playlist in the comments) #neurips #generativeai #genai #llm

  • View organization page for neptune.ai

    37,791 followers

    Maintaining AI infrastructure requires constant work—one that many ML/AI teams are forced to handle on their own. Keunwoo Choi shares the challenges AI teams face when training foundation models from scratch without dedicated infra support: → Role conflict: researchers take on infrastructure maintenance, often diverting focus from model development. → Choosing between GPU utilization vs. delivery: maximizing GPU efficiency is tempting (given the cost), but sometimes, the speed of iteration matters more. → Debugging nightmares: as GPU clusters scale, failures increase, and error messages rarely provide useful diagnostics. — Our upcoming report dives deeper into these challenges. Follow along for more insights! #generativeai #genai #llm #foundationmodels

  • View organization page for neptune.ai

    37,791 followers

    [New on our blog] Introduction to State Space Models as Natural Language Models by Jana Kabrit TL;DR → State Space Models (SSMs) use first-order differential equations to represent dynamic systems. → The HiPPO framework provides a mathematical foundation for maintaining continuous representations of time-dependent data, enabling efficient approximation of long-range dependencies in sequence modeling. → Discretization of continuous-time SSMs lays the groundwork for processing natural language and modeling long-range dependencies in a computationally efficient way. → LSSL, S4, and S5 are increasingly sophisticated and efficient sequence-to-sequence state-space models that pave the way for viable SSM-based alternatives to transformer models. — (link to the full article in the comments) #generativeai #genai #llm

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  • Training LLMs is hard. Training them efficiently is even harder. Here’s what experience has taught Stefan Mesken: → Curriculum design is tricky: deciding what data to use (and when) is one of the biggest optimization challenges. → Hyperparameter tuning matters (a lot): as models scale, they become even more sensitive. Getting this wrong can lead to costly inefficiencies. → Infrastructure is everything: building a supercomputer is closer to constructing a house than buying a laptop. Every detail impacts performance. → Software optimization is a game-changer: a dedicated HPC team can significantly boost training efficiency and unlock new capabilities in the inference pipeline. → Hiring the right team is a key investment: technical expertise across hardware, software, and research is critical to navigating the complexities of LLM development. — More insights like this will be featured in our upcoming State of LLM training report. Stay tuned! #generativeai #genai #llm

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Funding

neptune.ai 3 total rounds

Last Round

Series A

US$ 8.0M

Investors

Almaz Capital
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