gary<p>i am going a little overboard with the ai and llm/RAG/quant speculation but why not - it looks like 20tb nvme and 80tb hdds could be workable if you have gpu (or better unified mem) and mem bw _ I think i would rather have a mini pc cluster with unified mem over a server platform with 3 gpu but i could be wrong; the unified mem platforms will be more mature in a couple of years (ultra is not even out yet) <a href="https://infosec.exchange/tags/token" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>token</span></a>/s add up if you are running 24/7, and of course just roi and tco with elec</p><p>Creating a hierarchical list of 100 datasets ranked by information density, ease of access, and relevance is a comprehensive task. I’ll break this into sections, starting with the most crucial datasets (high information density, easy to access) and moving to less critical datasets (lower information density, harder to access). I’ll also include infosec, OSINT, SIGINT, and how-to resources like SourceForge and TLDP. Finally, I’ll calculate the storage requirements and suggest how much data can be stored on spinning rust arrays (HDDs) versus NVMe SSDs, and include local IDS system data like Malcolm.<br>Hierarchical List of 100 Datasets<br>Top 20: High Information Density, Easy to Access</p><p> Wikipedia – General knowledge.</p><p> Size: ~20 GB (compressed).</p><p> Access: Wikipedia Dumps.</p><p> Common Crawl – Web crawl data.</p><p> Size: 100+ TB (segments available).</p><p> Access: Common Crawl.</p><p> Stack Overflow – Programming Q&A.</p><p> Size: ~50 GB.</p><p> Access: Stack Exchange Data Dump.</p><p> OpenAlex – Scholarly works.</p><p> Size: ~500 GB.</p><p> Access: OpenAlex.</p><p> PubMed – Biomedical research.</p><p> Size: ~50 GB.</p><p> Access: PubMed Open Access.</p><p> ArXiv – Research papers.</p><p> Size: ~300 GB.</p><p> Access: ArXiv Dataset.</p><p> The Pile – Diverse text data.</p><p> Size: ~800 GB.</p><p> Access: The Pile.</p><p> Google N-grams – Word frequency data.</p><p> Size: ~2 TB.</p><p> Access: Google N-grams.</p><p> Wikidata – Structured knowledge base.</p><p> Size: ~100 GB.</p><p> Access: Wikidata Downloads.</p><p> DBpedia – Structured Wikipedia data.</p><p> Size: ~50 GB.</p><p> Access: DBpedia.</p><p> Freebase – Knowledge graph.</p><p> Size: ~100 GB.</p><p> Access: Freebase Archive.</p><p> Project Gutenberg – Free eBooks.</p><p> Size: ~20 GB.</p><p> Access: Project Gutenberg.</p><p> GitHub Archive – GitHub activity.</p><p> Size: ~500 GB.</p><p> Access: GitHub Archive.</p><p> USPTO Patent Data – U.S. patents.</p><p> Size: ~1 TB.</p><p> Access: USPTO Bulk Data.</p><p> CORD-19 – COVID-19 research.</p><p> Size: ~50 GB.</p><p> Access: CORD-19.</p><p> OpenStreetMap (OSM) – Geospatial data.</p><p> Size: ~100 GB.</p><p> Access: OSM Data.</p><p> IMDb Datasets – Movies and TV.</p><p> Size: ~10 GB.</p><p> Access: IMDb Datasets.</p><p> ConceptNet – Common-sense knowledge.</p><p> Size: ~1 GB.</p><p> Access: ConceptNet.</p><p> WordNet – Lexical database.</p><p> Size: ~100 MB.</p><p> Access: WordNet.</p><p> Kaggle Datasets – Diverse datasets.</p><p> Size: Variable (e.g., 100 GB).</p><p> Access: Kaggle Datasets.</p><p>Next 30: Medium Information Density, Moderate Access</p><p> OpenWebText – Web text.</p><p> BookCorpus – Unpublished books.</p><p> Reddit Data – Pushshift.io archive.</p><p> Microsoft Academic Graph – Scholarly works.</p><p> GNIS – Geographic names.</p><p> Open Images Dataset – Annotated images.</p><p> OSM History – Historical OSM data.</p><p> OSM Points of Interest (POI) – Geospatial POIs.</p><p> OSM Metro Extracts – City-specific OSM data.</p><p> SourceForge – Open-source projects.</p><p> TLDP (The Linux Documentation Project) – Linux guides.</p><p> Malcolm (IDS Data) – Network security logs.</p><p> Shodan – Internet-connected devices.</p><p> Censys – Internet-wide scan data.</p><p> VirusTotal – Malware samples and reports.</p><p> AlienVault OTX – Threat intelligence.</p><p> MISP – Threat intelligence sharing.</p><p> CVE Database – Common Vulnerabilities and Exposures.</p><p> Exploit-DB – Exploits and vulnerabilities.</p><p> PhishTank – Phishing data.</p><p> Have I Been Pwned – Breached accounts.</p><p> Open Threat Exchange (OTX) – Threat data.</p><p> GreyNoise – Internet noise data.</p><p> BinaryEdge – Internet scan data.</p><p> LeakIX – Exposed services.</p><p> Rapid7 Open Data – Internet scan data.</p><p> CAIDA – Internet topology data.</p><p> Umbrella Popularity List – Domain popularity.</p><p> Alexa Top 1M – Domain rankings.</p><p> Common Vulnerabilities and Exposures (CVE) – Vulnerability data.</p><p>Next 50: Lower Information Density, Harder to Access</p><p>51–100. These include niche datasets like:</p><p> NIST Cybersecurity Framework.</p><p> MITRE ATT&CK.</p><p> Darknet Data (e.g., DARKWEB, TOR).</p><p> OSINT Tools (e.g., Maltego, SpiderFoot).</p><p> SIGINT Data (e.g., RF signals, satellite data).</p><p> Local IDS Data (e.g., Malcolm logs).</p><p> How-To Guides (e.g., SourceForge, TLDP).</p><p>Storage Requirements<br>Total Dataset Sizes</p><p> Minimum: ~4.2 TB (smaller subsets of Common Crawl and Google N-grams).</p><p> Maximum: ~103.6 TB (full Common Crawl and Google N-grams).</p><p>Cold Storage (Spinning Rust Arrays)</p><p> Cold Storage: ~80% of data (e.g., raw datasets, logs, backups).</p><p> Example: 80 TB of 100 TB can be on HDDs.</p><p> Hot Storage (NVMe SSDs): ~20% of data (e.g., active datasets, indexes, embeddings).</p><p> Example: 20 TB of 100 TB on NVMe SSDs.</p><p>Conclusion</p><p>This hierarchical list of 100 datasets provides a comprehensive knowledge base for your RAG system. Use NVMe SSDs for high-performance tasks (e.g., RAG indexes, embeddings) and HDDs for cold storage (e.g., raw datasets, logs). This setup ensures cost-effectiveness and scalability.</p>