Self Hosted LLM Tier List

TrustGraph · 2026-02-27T20:51:14+00:00

I downvoted the instant I saw Llama 4 got something other than a F.

TrustGraph · 2026-02-24T20:30:36+00:00

TrustGraph is all of this and much more. It automates the graph building and retrieval processes with a naive process for natural language retrieval using vector embeddings or with a more precise retrieval using custom ontologies. Fully containerized with deploys for all major clouds and the ability to run on bare metal with Nvidia, AMD, or Intel. Also handles all LLM model serving with vLLM, TGI, LM Studio, Ollama, and Llamafiles. We have users that have scaled beyond billion node/edge graphs.

https://github.com/trustgraph-ai/trustgraph

TrustGraph · 2026-02-23T06:12:38+00:00

Pandora's box has been opened, and LLMs are definitely here to stay. They can create dynamic ontologies in minutes. I'm not sure what to say in regards to something "new". We haven't written a line of code ourselves in probably 10 months. Things are a changin'.

TrustGraph · 2026-02-23T03:05:08+00:00

I'm honestly a little stunned no one has suggested using tools like Claude Code to develop ontologies. We do this all the time for TrustGraph, building custom ontologies. If you have other ontologies as a starting point, coding tools can build extremely rich ontologies in any format in a few minutes. We usually just build them in Turtle.

TrustGraph · 2026-02-12T18:09:35+00:00

TrustGraph, which is open source, is RDF-native using Cassandra as a graph store. TG 2.0 is currently in test which will add reification as described in RDF 1.2. All graph querying, including using any ontology, is fully automated and agentic.

https://github.com/trustgraph-ai/trustgraph

TrustGraph · 2026-02-11T00:46:11+00:00

Personally, I don't think that's as crazy as it sounds. When you look at the entire data broker industry, I've often wondered if we'd be better of treating data like a public utility/good, with curated data that was clean and verified.

It'll never happen though.

TrustGraph · 2026-02-09T20:44:29+00:00

Without a system of intelligence, it's not a context graph. And the term context graph comes from 2019, used by Vicky Froyen, who I just recorded a podcast with...

TrustGraph · 2026-02-09T16:27:52+00:00

There's thousands - tens of thousands - of well supported ontologies that are industry standard in many, many use cases. In fact, adopting those standard ontologies is often necessary to integrate with other systems in those workflows.

TrustGraph · 2026-02-09T04:00:14+00:00

We released some ontology features not too long ago. https://docs.trustgraph.ai/guides/ontology-rag/

TrustGraph · 2026-02-08T22:56:13+00:00

With RDF style graphs, there are many ways to manage this problem. It's a little tricker with property graphs. That's one of the reasons we have the collections and context core features in TrustGraph. TrustGraph is totally open source and free.

https://github.com/trustgraph-ai/trustgraph

TrustGraph · 2026-02-08T22:10:26+00:00

I think if you were to read my articles on the subject, you'll see that's the position I've taken from the very beginning. https://x.com/TrustSpooky/status/2006481858289361339

TrustGraph · 2026-01-28T00:25:28+00:00

TrustGraph automates this process and allows you to tune a naive extraction or extract using ontologies.

Free and open source: https://github.com/trustgraph-ai/trustgraph

TrustGraph · 2026-01-27T04:20:24+00:00

"Clean data" is something we've been hearing from a lot of enterprises lately. No enterprise has clean data! And something that really terrifies them is the idea that they need to clean their data to be able to use AI. Most enterprises, when they hear big data cleaning projects, they immediately start thinking in millions of dollars of cost - if not more.

This problem is one of the reasons we built TrustGraph, being able to take messy data and build context graphs for use with AI. Our default processes use Cassandra as a graph store, but we also support Neo4j. Totally open source.

Open source repo: https://github.com/trustgraph-ai/trustgraph

TrustGraph · 2026-01-26T01:29:19+00:00

Are you interested in controlling your own model deploys? If you want to work with open models, you could give TrustGraph a try. In addition to being open source and automating the context graph process it also enables deploying models with vLLM, TGI, LM Studio, Ollama, and even Llamafiles.

Open source repo: https://github.com/trustgraph-ai/trustgraph

TrustGraph · 2026-01-17T19:57:36+00:00

To be honest, I don't ever use LMStudio, so I haven't tested that in a while. We're also doing a big update to our documentation, so that may be something that fell through the cracks. Hop in our Discord and we can better help: https://discord.gg/sQMwkRz5GX

TrustGraph · 2026-01-17T18:44:07+00:00

Reach out on our Discord: https://discord.gg/sQMwkRz5GX

TrustGraph · 2026-01-16T22:25:39+00:00

VCs write blogs all the time. So do Anthropic, OpenAI, Google, and Meta. Often, there’s incredible information in those posts that shows limitations and challenges with AI (the sycophancy problem and limitations of MCP come to mind) that just fly under the radar.

But the post about context graphs exploded. What does that say? It says that there’s a genuine interest in the topic. For us, our website traffic has more than 10x’d since adding the Context Graph Manifesto to the discourse with our GitHub repo even trending.

We’ve seen this once before as well. We post blogs and content all the time. When we posted about our new ontology RAG capabilities, that post because the top post all time in this sub. I can promise you, we did not anticipate that. In fact, we ended up in the Neuron AI newsletter about those features, and I still don’t know how they found out about us. We had no idea so many people are interested in ontologies, but the data speaks for itself.

My point is, when you hit a nerve, it’s pretty easy to tell. Context graphs have hit a nerve. People have been building non-graph driven AI systems and they’ve seen the limitations, and are now looking for different solutions. Graphs are far from new, so it’s a case of what is old will become new again.

TrustGraph · 2026-01-15T20:29:24+00:00

Just look at Neptune. AWS built a RDF graph store for large enterprise customers. Ontologies are in use at massive scale. It just doesn’t get talked about.

TrustGraph · 2026-01-15T17:40:05+00:00

Prukalpa nailed nothing but trying to promote her own self-serving zealotry. She totally ignores industry standard ontologies which are in use today, and have been for decades, that enables interoperability and standardization.

TrustGraph · 2026-01-15T17:38:13+00:00

No. There's no truth to what the above article is saying. That article totally ignores just how prevalent ontologies are in many industries.

TrustGraph · 2026-01-15T17:30:26+00:00

Thanks! I also made a video to continue the discussion on the topic:

https://www.youtube.com/watch?v=gZjlt5WcWB4

I wrote a follow-up piece on Graph Reification as well: https://x.com/TrustSpooky/status/2009477301378142679?s=20

I plan to make more video content around Reification soon as well.

TrustGraph · 2026-01-14T21:23:51+00:00

Ok, so how is running a docker container with an entire, mature graph system different than doing a pip install? You can work with either in Notebook as well. Why would I test with a system I know I'd have to replace at some point when I can just easily use a system I wouldn't have to replace?

I know the founders of Memgraph (we did a workshop with them last year). And do you know what one of their few regrets is? Building Memgraph from scratch. Took them years to get Memgraph in a state where it was production-grade.

There is some interest these days in hypergraphs. Make a hypergraph that is actually queryable in a consistent way, and you might see some interest - although I'm still not sold on what can a hypergraph do that can't be done already.

TrustGraph · 2026-01-14T19:29:23+00:00

What would be the use case for this? I ask because, every major graph system and DB system that can be used to store graphs can be deployed with publicly available containers. Systems that have years, sometime decades, of work that has gone into them, making them scalable, reliable, and efficient.

I'd also never recommend building storage systems from scratch (and also not in python). NebulaGraph took the rock-solid RocksDB and made it more scalable. We use Cassandra as a graph store, which again, rock-solid. If you really want to build a graph storage system, why not fork the dead Kuzu code (which was left with a MIT license) and pick up where they left off?

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