ollama VS llama

Download: Go to https://ollama.com/ and click on the download link for your operating system.

Most AI resume tools call out to OpenAI or Anthropic and charge you for every request. Persona supports Ollama — which means you can run the AI model locally on your own hardware, with zero API costs and zero data leaving your machine.

To solve these, we built a stack that prioritizes integrity over ease. The centerpiece is Ollama, running the mxbai-embed-large model locally. This is the engine that translates human thought into high-dimensional coordinates.

Ollama for model serving

Fully offline: Point it at Ollama or LM Studio. Zero cost, nothing leaves your network.

Recommended hardware: The RTX 3060 with 12 GB VRAM is the budget king here — all these models fit with room to spare for KV cache overhead, even Gemma 4:12B (which needs ~8.5–9 GB with overhead). An RTX 4060 Ti 16 GB gives you more headroom. On the Apple side, any M2 or M3 MacBook with 16 GB unified memory handles these models comfortably via Ollama's Metal backend.

This guide shows how to swap out every cloud API with a local Ollama server running qwen3-coder:30b. Same tools, same workflows, no data leaving your network.

Related: 35B MoE on 2× 1080 Ti · Ollama

The sample is a tiny console app running entirely against a local Ollama model — no cloud keys, and every HTTP call is traced so I can see exactly what goes over the wire (complete sample code). There's a single skill on disk:

irm https://ollama.com/install.ps1 | iex

What are your thoughts on the origin of the LLaMA leak? It's interesting that the training data was torrented, and so was the leak. Perhaps we will never know? For the OSINT folks, not a lot to go on, or maybe a lot, depending?

https://en.wikipedia.org/wiki/Llama_(language_model)#Leak

https://archived.moe/g/thread/91848262#p91850335

https://github.com/meta-llama/llama/pull/73/files

Llama 2-Chat

preceding calendar month, you must request a license from Meta...

ref: https://github.com/meta-llama/llama/blob/main/LICENSE

But again, not open source...

IANAL But, this is not true it would be a piece of the software. If there is a copyright on the app itself it would extend to the model. Even models have licenses for example LLAMA is release under this license [1]

[1] https://github.com/meta-llama/llama/blob/main/LICENSE

Hello Hacker News, Yagil here- founder and original creator of LM Studio (now built by a team of 6!). I had the initial idea to build LM Studio after seeing the OG LLaMa weights ‘leak’ (https://github.com/meta-llama/llama/pull/73/files) and then later trying to run some TheBloke quants during the heady early days of ggerganov/llama.cpp. In my notes LM Studio was first “Napster for LLMs” which evolved later to “GarageBand for LLMs”.

What LM Studio is today is a an IDE / explorer for local LLMs, with a focus on format universality (e.g. GGUF) and data portability (you can go to file explorer and edit everything). The main aim is to give you an accessible way to work with LLMs and make them useful for your purposes.

Folks point out that the product is not open source. However I think we facilitate distribution and usage of openly available AI and empower many people to partake in it, while protecting (in my mind) the business viability of the company. LM Studio is free for personal experimentation and we ask businesses to get in touch to buy a business license.

At the end of the day LM Studio is intended to be an easy yet powerful tool for doing things with AI without giving up personal sovereignty over your data. Our computers are super capable machines, and everything that can happen locally w/o the internet, should. The app has no telemetry whatsoever (you’re welcome to monitor network connections yourself) and it can operate offline after you download or sideload some models.

0.3.0 is a huge release for us. We added (naïve) RAG, internationalization, UI themes, and set up foundations for major releases to come.

derivative works thereof).”

https://github.com/meta-llama/llama/blob/b8348da38fde8644ef0...

Also even if you did use Llama for something, they could unilaterally pull the rug on you when you got 700 million years, AND anyone who thinks Meta broke their copyright loses their license. (Checking if you are still getting screwed is against the rules)

Therefore, Zuckerberg is accountable for explicitly anticompetitive conduct, I assumed an MMA fighter would appreciate the value of competition, go figure.

One thing I wanted to add and call attention to is the importance of licensing in open models. This is often overlooked when we blindly accept the vague branding of models as “open”, but I am noticing that many open weight models are actually using encumbered proprietary licenses rather than standard open source licenses that are OSI approved (https://opensource.org/licenses). As an example, Databricks’s DBRX model has a proprietary license that forces adherence to their highly restrictive Acceptable Use Policy by referencing a live website hosting their AUP (https://github.com/databricks/dbrx/blob/main/LICENSE), which means as they change their AUP, you may be further restricted in the future. Meta’s Llama is similar (https://github.com/meta-llama/llama/blob/main/LICENSE ). I’m not sure who can depend on these models given this flaw.

It looks like Llama 2 7B took 184,320 A100-80GB GPU-hours to train[1]. This one says it used a 96×H100 GPU cluster for 2 weeks, for 32,256 hours. That's 17.5% of the number of hours, but H100s are faster than A100s [2] and FP16/bfloat16 performance is ~3x better.

If they had tried to replicate Llama 2 identically with their hardware setup, it'd cost a little bit less than twice their MoE model.

[1] https://github.com/meta-llama/llama/blob/main/MODEL_CARD.md#...

Ironically, the LLaMA license text [1] this is lifted verbatim from is itself copyrighted [2] and doesn't grant you the permission to copy it or make changes like s/meta/dbrx/g lol.

[1] https://github.com/meta-llama/llama/blob/main/LICENSE#L65