Cracking open
AI's black box

The NSF National Deep Inference Fabric provides free remote access to large-scale AI models, enabling researchers and students to perform transparent, reproducible experiments on model internals.

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Learn About the Fabric

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Research Papers

Open-source Repos

GitHub Stars

GPUs

Data sourced from NDIF internal metrics and public GitHub repository statistics.

What is NDIF?

The National Deep Inference Fabric is a unique nationwide research computing fabric that enables scientists to perform transparent and reproducible experiments on the largest-scale open AI systems. NDIF has three parts:

HPC Fabric

A nationwide high-performance computing fabric powered by NCSA's Delta — utilizing one 8xH200 node and six 4xA40 nodes — providing free remote access to run experiments on large-scale AI models.

NNsight Library

An open-source PyTorch-based toolkit (850+ GitHub stars) that lets researchers inspect, modify, and customize internal computations of AI models, complete with remote access to large scale models.

Training Program

A nationwide training program developed with PIT-UN, a consortium of 63 universities and colleges, providing workshops, tutorials, and resources to build a broad community of AI researchers.

Learn more about the Fabric

Remote Model Access

Access and inspect open-source model internals remotely on NDIF with the NNsight API. Deploy models on-demand with full transparency into their computations.

All models are free for research use — no GPU required on your end.

View All Models NNsight Docs ↗

Hot70B

Llama 3.1 70B

meta-llama/Meta-Llama-3.1-70B

Hot8B

Llama 3.1 8B

meta-llama/Meta-Llama-3.1-8B

Warm405B

Llama 3.1 405B

meta-llama/Meta-Llama-3.1-405B

Warm671B

DeepSeek R1

deepseek-ai/DeepSeek-R1

Featured Research

Researchers worldwide use NDIF and NNsight to uncover how large-scale AI models work, with 110+ published papers at top venues including ICLR, NeurIPS, ICML, and EMNLP.

ICLR 2026

Activation Steering via Generative Causal Mediation

Aruna Sankaranarayanan, Amir Zur, Atticus Geiger, Dylan Hadfield-Menell

Researchers tackle the challenge of pinpointing and controlling specific behaviors in language models, which can be dispersed throughout lengthy responses. By developing a new approach, they aim to provide more precise control over these models, enabling more effective intervention and modification of their outputs. This work has implications for improving the reliability and trustworthiness of language models in real-world applications.

EACL 2026

Can you map it to English? The Role of Cross-Lingual Alignment in the Multilingual Performance of LLMs

Kartik Ravisankar, HyoJung Han, Sarah Wiegreffe, Marine Carpuat

Researchers investigate the role of linguistic and cultural biases in machine translation systems, shedding light on how these biases can affect the accuracy and fairness of translations. By examining the intersection of language, culture, and technology, this study aims to promote more inclusive and equitable language technologies. Its findings have implications for the development of more culturally sensitive machine translation systems.

ArXiv 2026

Counting Hypothesis: Potential Mechanism of In-Context Learning

Jung H. Lee, Sujith Vijayan

Researchers are working to understand how large language models can learn specific tasks from just a few examples, a phenomenon known as In-Context Learning. This ability has the potential to expand the use of these models into new areas, but its underlying mechanisms are still not well understood, making it difficult to correct errors or diagnose issues. By proposing a new hypothesis, the "counting hypothesis," this study aims to shed light on how these models support In-Context Learning.

Pattern Recognition 2026

DFWe: Efficient Knowledge Distillation of Fine-tuned Whisper Encoder for Speech Emotion Recognition

Y Ma, X Jiang, J Sang, R Li

Researchers are exploring ways to adapt powerful pre-trained speech models, like Whisper, to recognize emotions in speech, a task that requires more nuanced understanding than just acoustic modeling. By addressing the limitations of these models in capturing emotional cues, this work aims to improve speech emotion recognition. This advancement could lead to more empathetic and human-like interactions with voice-based systems.

ArXiv 2026

Disentangling meaning from language in LLM-based machine translation

Théo Lasnier, Armel Zebaze, Djamé Seddah, Rachel Bawden, Benoît Sagot

Researchers are working to understand how Large Language Models (LLMs) work by developing new methods for Mechanistic Interpretability (MI), which aims to explain the inner workings of neural networks. This effort is crucial for building trust in AI systems and identifying potential biases or flaws. By shedding light on how LLMs process and generate language, MI can help improve the reliability and transparency of these powerful models.

ICLR 2026

Do Transformers Use their Depth Adaptively? Evidence from a Relational Reasoning Task

Alicia Curth, Rachel Lawrence, Sushrut Karmalkar, Niranjani Prasad

Researchers explore how transformers, a type of AI model, adapt their processing depth to tackle tasks of varying complexity. By analyzing how these models process information across different layers and tokens, they shed light on the strategies transformers use to solve problems. The study reveals that transformers can adapt their depth to suit the task at hand, especially when fine-tuned for specific tasks, allowing them to efficiently allocate processing resources.

View all research

Featured Code

Open-source repositories using NDIF and NNsight — production-grade interpretability tools, libraries, and research code from the community.

PopularActiveCourse

callummcdougall/ARENA_3.0

No description

Jupyter Notebook1,124728

Active 2 days ago

PopularActive

hijohnnylin/neuronpedia

open source interpretability platform 🧠

TypeScript912120

Active 2 days ago · MIT

PaperPopular

saprmarks/dictionary_learning

No description

Python421100

Active 10 months ago · MIT

PaperPopularActive

EleutherAI/delphi

Delphi was the home of a temple to Phoebus Apollo, which famously had the inscription, 'Know Thyself.' This library lets language models know themselves through automated interpretability.

Python26058

Active 4 days ago · Apache-2.0

PopularActive