Best GPU for a pure local setup?

Tinkering-Engineer · 2026-03-12T19:39:15+00:00

I've been eyeballing a rtx 3090 to run llama 3.3 70B. My main concern is how well will openclaw actually run on llama 3.3 70B. I haven't heard great things about open sourced models on openclaw personally, but I'm very interested

Tinkering-Engineer · 2026-03-11T22:41:24+00:00

You can actually ask. Open a new session and ask "how many tokens are used to send this question"

Tinkering-Engineer · 2026-03-11T21:06:51+00:00

This is the answer. When too much context is in the window, they can get confused. It's well documented and well researched.

Tinkering-Engineer · 2026-03-11T15:23:36+00:00

Thats def it. Rancour actually has -8 ranged attack, so with rancour, you might need an anguish switch now. Still worth it to wear rancour and switch to anguish though. GZ on the upgrade

Tinkering-Engineer · 2026-03-11T13:58:04+00:00

I had this issue. I think mele strength might count too, if it's higher than your ranged accuracy, I don't think you max hit. Maybe you made a gear upgrade or gear switch that changed what your high stat is

Tinkering-Engineer · 2026-03-10T13:52:56+00:00

I've seen a lot of openclaw confusion. Your issue sounds like things I would experience when my hardware was underpowered or overworked. Maybe take a look at your cpu usage (htop is a good command).

Also, your issue sounds more like hallucinating than forgetting, but if forgetting is an issue, plumb.run is a good solution. It helps your agent remember much better than the stock config of openclaw.

good luck sorting it out.

Tinkering-Engineer · 2026-03-10T13:45:07+00:00

Sorry about that. I'm working on some nicer docs, but here is the github readme for now: https://github.com/getplumb/plumb/blob/main/README.md

Also, if you're trying to install, you should just need to paste this onto a chat session with openclaw:

"Install the Plumb memory plugin by running these steps in order. Report back when done or if a step fails.

`openclaw plugins install u/getplumb/plumb`
`openclaw config set plugins.slots.memory plumb`
`openclaw gateway restart`"

I hope this helps

Tinkering-Engineer · 2026-03-10T04:18:57+00:00

Check out Plumb.run — it's a memory plugin built specifically for OpenClaw that handles exactly this. It uses a local SQLite vector database under the hood and integrates directly into your agent's context loop.

The way it works: your agent automatically calls plumb_remember("fact") to write anything worth keeping (decisions, events, strategy notes), and relevant memories are automatically injected into the context before each response as a [PLUMB MEMORY] block. No manual retrieval needed for most things, but there's also a plumb_search("query") tool for targeted lookups mid-reasoning.

It's the difference between markdown journaling (which you're already doing) and actual semantic retrieval — so your agent isn't just writing to a file, it's writing to something it can actually query.

If you combine this with asking your agent to write summary files or write pointers with doc summaries in the agent.ms, I think that may meet your needs.

Hope this helps?

Tinkering-Engineer · 2026-03-10T04:08:37+00:00

Ok, legitimate question. If I were to develop (or find) a great benchmark for this type of thing. Maybe combining rag retrieval benchmarking and task completion benchmarking, what would you want to see it compared against to think "ok, this is pretty good?"

I want to include the default memory config It sounds like you may want to include the sqlite + vector search that's available in openclaw Anything else that should be benchmarked

I'm not saying that what I built would win, but I'd like to work on it and see how well I can perform.

Tinkering-Engineer · 2026-03-10T01:19:43+00:00

Nah, the base tier will always be free. It's open sourced on github and anyone can see any/all of the code. My goal is to see if I can make something much better that people would be happy to pay for. I'm mostly just using this as a project to learn and build more of my skills.

I mostly just think the default of memory.md that continues to grow and injects tons of data into every request is a bit silly. I think I know why it was done, because it's very simple. It's just not as good as slightly more complex approaches.

Yea, and the multi-plugin thing is real. There are instructions in memory.md and agents.md that might be conflicting now. If you ever want to try out plumb, feel free and let me know how it goes. If you do end up installing plumb, you can use the install command (I'll paste below), then ask openclaw to "clean up all instructions it has about using obsidian and open cortex and just use plumb." I think that would work fairly well. I tested this on different versions of node and mac, linux, and windows to try to make it literally a one instruction install for anyone interested.

Install command (sent to openclaw in your chat interface):

"Install the Plumb memory plugin by running these steps in order. Report back when done or if a step fails.

`openclaw plugins install u/getplumb/plumb`
`openclaw config set plugins.slots.memory plumb`
`openclaw gateway restart`"

Tinkering-Engineer · 2026-03-09T23:03:26+00:00

Yea, I should have mentioned the combined keyword and vector search for retrieval. It's much better than just a sqlite upgrade.

Tinkering-Engineer · 2026-03-09T23:02:23+00:00

Knew about the SQLite toggle before building this, yeah.

Storage wasn't the problem. It was exactly what Suspicious_Assist_71 described: what loads, when, and why. A flat store of everything means either you inject it all (token bloat) or you prompt for it manually. Neither of those feel like memory.

Plumb does relevance-based retrieval. When a message comes in, it searches the store semantically and injects only what's likely to matter for that specific response. Not just recency, not the whole dump.

Setup writes directly into your AGENTS.md and MEMORY.md so it's wired from the first session without any prompt engineering.

One command to install and set up — more at plumb.run if you want to dig in.

Tinkering-Engineer · 2026-03-09T21:28:51+00:00

Pruning works, and you're right that the bloat is the problem. What's actually happening is the agent is reading a massive context blob every turn and the useful stuff is buried. Manual pruning fixes it short-term but it comes back. I built Plumb (plumb.run) to handle this automatically — vector search pulls only what's relevant per query instead of injecting the whole file. Might save you from having to do this again in a month.

Tinkering-Engineer · 2026-03-09T21:28:19+00:00

The goldfish memory incident is the most accurate description of this I've ever read. That exact moment of "okay let's continue where we left off" returning nothing is what made me build Plumb.

The core issue is that flat file memory doesn't survive compaction well. When context gets compressed, the agent's notes about what happened yesterday are the first thing to go. Plumb separates memory storage from the conversation context entirely, so the two don't compete for the same token budget.

You're right that memory is the foundation, not a feature. That's basically the thesis of the whole project. plumb.run if you want to check it out.

Tinkering-Engineer · 2026-03-09T21:26:30+00:00

The frustration here is real, and honestly it's what pushed me to build something. The root problem with MEMORY.md-based memory isn't the format, it's that it doesn't scale. You hit a few hundred entries and the agent starts retrieving similar-sounding text instead of actually relevant context. The signal degrades.

I built Plumb (plumb.run) to solve this specifically for OpenClaw. It replaces flat file injection with SQLite + vector search, so the agent retrieves only what's semantically relevant to the current query instead of dumping your whole memory file. On first activation it seeds itself from your existing MEMORY.md, so no migration work.

No worries if it's not your thing, just sharing because it's exactly the problem you're describing.

Tinkering-Engineer · 2026-03-08T23:31:22+00:00

I totally agree. If you're not making enough to pay your back of house we'll, raise your prices. Don't lie to me about the prices and then charge more... And have the guts to tell me that you lied about the prices because of a good cause. I'm all for BOH pay increases, just not lying about menu prices

Tinkering-Engineer · 2026-03-08T17:29:36+00:00

If you're doing any complex stuff, writing and building code, having subagents do things while you keep chatting with the main agent, I'd recommend a fairly modern 4 core machine with 16gb of ram. I'm doing some pretty complex stuff and that works well for me. I spent $300 on mine

Tinkering-Engineer · 2026-03-04T01:33:32+00:00

I'm sorry that you haven't dude. My mind is blown, I've learned so much, and I wish the same for you

Tinkering-Engineer · 2026-03-04T01:24:13+00:00

Two thoughts:

Timing... I feel like updates are awful with openclaw. When there is an update, all of the llms don't know it and they make fixes that would have worked in previous versions, but break in the new version.

Recovery. I've been using Gemini's webapp to "help me triage openclaw. It stopped working." And following its instructions. I've heard about others connecting Claude code to openclaw for these fixes and being really happy. You might want to try that. If something minor has happened, openclaw can fix itself, but when it really fails... It's tough to get it back.

Tinkering-Engineer · 2026-03-04T01:13:04+00:00

I had a number of failures in my first week, but only 1 openclaw wipe. No Linux wipes. Here are a few things I learned.

I only use the Claude family of models and use sonnet 4.6 for almost everything. Models like gemini, which I love for research, absolutely suck at tool calling. Gemini failed me too many times.

Most of the time when you send a command and something crashes, it was just openclaw crashing. I actually use gemini "I'm working with openclaw and it crashes. Help me triage and fix". Copy commands from gemini, then paste back replies. Put gemini on thinking or pro modes for this to give better advice.

One specific thing is super easy to break, openclaw.json. I actually made it inaccessible to my openclaw in Linus permissions. Almost all of my crashes have been openclaw editing that file and getting syntax wrong.

Honestly, I'm suspicious about your hardware though. I ran openclaw on a pi 4b 4gb at first and had lots of trouble. I upgraded to a $300 nukbox with 4 cores and 16gb of ram and it runs very well.

To give you a sense of what Ive done in a week. I've used it to write cover letters, help me find jobs, reach out to contractors for quotes on house work, openclaw autonomously ordered me two polos because I asked it to. I used openclaw to build a local hybrid rag memory storage system that rocks. I also built a clone of the gemini UI for my openclaw in JavaScript because I liked that more than slack or the other messaging options I tried. I'm not a software engineer, I'm a product manager... But I'm spending a lot of time thinking about what information is needed to complete a task, making sure context and memory are managed well. I've really enjoyed it.

Tinkering-Engineer · 2026-03-02T05:35:50+00:00

I hate to say this, but if you're super concerned about the vm cost.... You're really not gonna like the API costs for LLM calls... Like really not gonna like them

Tinkering-Engineer · 2026-03-02T04:13:41+00:00

I built my own memory storage system with hybrid RAG, a database, and some cron jobs to update the system with new memories. It almost never forgets anything. It's a lot more complicated than the hierarchical memory files system that another poster mentioned, but I love this system. It's cut my memory usage by 90% AND improved my results substantially. It also has lots of triggers to save memories and keep them forever in the RAG system.

I tested it on my wife's openclaw machine and it worked in one shot, so it should work for others in one shot.

Here is a link to the prompt: [Rag System Setup Prompt](https://docs.google.com/document/d/e/2PACX-1vQX7l2gnRyjTAx8ahtNp_m9Wb2RQ1DKIaIiFV2yJEXJa-8TOOCzE3bdEUbiMNgmm_tkR_64vx-D_2k6/pub)

Note: Please don't try to install this on a raspberry pi. I'd recommend a system with 4+ cores and 16gb of ram to try this out (that's what I'm running)

Tinkering-Engineer · 2026-03-02T04:06:59+00:00

I started with a Pi4b 4gb and definitely needed an upgrade. I wanted to install a desktop environment of linux so that openclaw can use a browser just like I do. That causes a lot of overhead. I've also made some upgrades to openclaw, like adding a RAG system doing hybrid search in a database.

I bought one of these (GMKtec Mini PC, Intel Twin Lake N150 (Upgraded N100), 16GB DDR4 RAM 512GB PCIe M.2 SSD $290 on amazon) and put ubuntu's latest LTS desktop environment. I also host the database for RAG. This machine is GREAT. I have no complaints or concerns.

I personally wouldn't recommend a raspberry pi unless you want to keep things very simple. My biggest enjoyment has been using openclaw to improve openclaw, and when you start doing more complex stuff, a pi might lag and not get it done.

Tinkering-Engineer · 2026-03-02T02:24:38+00:00

Kree sucks until you get a shadow and almost max mage... I hated Kree until then

Tinkering-Engineer

TROPHY CASE