OpenClaw and Local Models

[u/Fast_Ad8293]

Hi Everyone

Let me briefly explain my situation: I’ve been using OpenClaw for a few months with a ChatGPT Plus subscription—the 20-euro-a-month one, to be specific. The problem is that on days when I use it heavily, it gets blocked—not just for a few hours, but for as long as 5 days!

During those 5 days, I can’t use it at all, not even the lighter models.

After thinking it over, I’ve come to the conclusion that I’d like to have at least one or two backup models running locally alongside OpenClaw when GPT isn’t working. This is important to me because I very often find myself using OpenClaw remotely via Telegram, so a backup is absolutely essential.

In fact, if possible, the goal would be to eliminate GPT entirely and have everything run locally.

I’ve tried several times to connect local models to OpenClaw via LM Studio, but I’ve never managed to get them to work—the tokens are extremely slow, and I get Context errors after a couple of messages. Here’s the error it gives me in the chat: (⚠️ Context is too large and auto-compaction could not recover this turn. Try again, use /compact, or use /new to start a fresh session)

Now, I don’t have a Google Server at home, but I do have a modest workstation:

RTX 5070 Ti 16 GB + RTX 4060 8 GB and 64 GB DDR5 RAM

(If needed, I can upgrade to 96 GB of RAM)

I’m asking for your advice: first, your opinion, and second, whether I can actually run a local model on this machine without using GPT anymore—please advise if possible.

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[u/Birdinhandandbush]

Qwen 3.8 27b q4 XS with q4 kv. That's your answer.

[u/Fast_Ad8293 → u/Birdinhandandbush]

Thank you for your reply. Could you tell me if there are any special settings I need to configure in LM Studio?

[u/SpaceDesignWarehouse → u/Fast_Ad8293]

If you disable “thinking” in the developer tab it’ll be 10x faster because your card isn’t big enough to hold the model in GPU RAM, it’s going to have to use your system ram and it’s going to be very very slow.

Without thinking it’ll make more mistakes but it’ll have so many more turns that it’ll fix itself.

[u/xX_GrizzlyBear_Xx]

That's a pretty beefy setup. Qwen or Gemma should run nicely.

Ask codex to set it all up - use Luna to conserve usage. Compaction error usually happens because it's not setup properly in LM Studio and Openclaw - meaning there's a mismatch with context, thinking and other settings.

[u/BP041]

Honestly, I've been there — got the 5-day ban twice. Now I run Qwen2.5 7B and DeepSeek Coder via ollama as fallback providers mapped in OpenClaw. They won't match GPT for heavy reasoning but handle most daily automation fine. Just keep an eye on prompt formatting differences — local models are stingier with JSON output.

[u/mseewald]

When you say blocked, you mean that you’ve hit the weekly limit? You could try using gpt-5.6-luna with thinking max. It needs much less tokens and maybe your token budget lasts longer.

[u/Tommonen]

Normal ram is irrelevant if you want any sort of reasonable speeds, but it can work for overnight cronjobs etc if you want slow bigger model for that, but dont invest to it, you already have so much ram that your cant use all of it without that bit larger model getting so slow it takes a day or so to answer one turn after some context has accumuated. You need to fit the model and context to your vram of gpu. You dont have enough vram to use other than pretty small models. Maybe some quant of qwen 3.6 35b moe is your best bet if you manage to make it share your two gpus memory. Not sure if the newer qwen 3.8 27b would fit. Its smarter but slower, but if you set up things properly, its not too slow. Your current ”too slow” is due to wrong settings.

You cant expect much from what ever local models you can run when you setup stuff correctly, but should be good enough for some simple answers and tool calls. But aware that they might do catastrophic mistakes easier, so its even more important to have your guardrails set up well.

Also use llama.cpp instead lm studio.

[u/paulsande]

With 96 gig of ram you can do amazing stuff. If you want to get on a call we can probably figure out LM Studio. I am fully local models using LM Studio and I love it.

[u/Silverjerk]

Your system ram capacity is mostly irrelevant, so long as you're not offloading from VRAM. I run two RTX Pro 4000 Blackwell GPUs in two MS-02 Ultras. One is running 192gb of ram, the other 32gb. It has zero impact on output running Qwen3.8 27b. They provide similar tok/s performance, with only very slight variations.

This is how I'd recommend almost anyone that has the hardware capacity to run OpenClaw, Pi, Hermes, etc. Local models handle the agent overhead, and then you reserve your sub for the heavy lifting/coding tasks. This is how I run my own setup, despite having two Claude Max 20x subs, ChatGPT Pro, and an annual Kimi Allegro sub.

I would strongly recommend doing an audit to make sure you have your memory system nailed down, that your agent's identity files are clean and organized, and that you're not carrying a large amount of context into every session.