#Local llms for low end devices

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slate lynx
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I am trying to setup openclaw on my laptop locally. Which small llm i shoukd use?

My laptop specs are 3050 gpu 4gb vram, intell 11th gen i5 cpu, 24 gb of ram.

Os- windows11.

rapid egretBOT
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spice summitBOT
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With a 3050 (4 GB VRAM) + 24 GB RAM, you can run a local model, but for OpenClaw-style agent + tools, the hard part isn’t “can it run” — it’s tool-calling reliability. Most small/local models will feel fine for chat, but can be flaky with structured tool calls.

What to aim for (given your specs)

  • Best “local” target: an instruction-tuned 7–8B model in 4‑bit quant
  • Keeps VRAM/RAM use reasonable on 4 GB VRAM GPUs
  • Set a smaller context (e.g., 4k) to avoid memory spikes
  • If you want it faster / more reliable on your machine: try a 3–4B instruct model (also 4‑bit), but expect noticeably weaker reasoning/tool use.

How to run it on Windows 11

  • Use a local server like Ollama / LM Studio / llama.cpp-based server, with CUDA enabled if stable.
  • If you hit VRAM limits: reduce context, use 4‑bit quant, or allow partial CPU offload.

Practical expectation

  • If your goal is “agent that uses tools reliably”: you’ll likely have a better time using a cloud model for the agent brain, and keep local models for lighter chat or experiments.
  • If your goal is “everything offline”: go with 7–8B 4‑bit + low context, and be prepared for more manual babysitting.

Docs (plus community’s up-to-date picks):

slate lynx
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Which local models you can suggest ? I need everything offline. I have lfm2.5 thinking

spice summitBOT
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If you need everything offline on a 3050 4 GB, I’d pick based on size + quant + context, not a specific “best model name” (those change weekly and I’ll just mislead you). Here’s what consistently works on your hardware:

What to try (offline-friendly targets)

  • Primary tier: 7–8B instruct model in 4‑bit quant (Q4 / int4)
  • Set context ~4k (8k often pushes memory hard on 4 GB VRAM)
  • Expect some CPU offload with a 3050 4 GB; that’s normal
  • If 7–8B is too slow / doesn’t fit well: 3–4B instruct in 4‑bit
  • Faster + more likely to stay within VRAM/RAM
  • Weaker reasoning, but often “good enough” for offline assistant basics

About “LFM2.5 thinking”

“Thinking” variants (any model marketed as think/reason/deep) tend to be slower and often more memory-hungry. On your GPU, you’ll usually get a better experience with the regular instruct/chat variant first, then try “thinking” only if performance is acceptable.

Important OpenClaw-specific caveat (offline)

Local models often struggle with structured tool calling (they may output almost-correct JSON, omit fields, etc.). If your goal is a fully autonomous agent using lots of tools, offline will work but expect more babysitting.

Where to get current names that fit these tiers

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Ask in #models and mention: “Windows 11, 3050 4GB, want 7–8B Q4 @ 4k ctx (or 3–4B if needed), fully offline.”
Docs: https://docs.openclaw.ai/concepts/models (see also the community notes section)

If you tell me what you’re using to run locally (Ollama vs LM Studio vs llama.cpp) and whether you care more about speed or quality, I can give the right quant/context/offload settings for that stack.

slate lynx
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I am using ollama. For usage I need it to target accuracy but speed should be in usable range

spice summitBOT
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For Ollama + fully offline on a 3050 4 GB VRAM, the sweet spot for accuracy (but still usable speed) is:

1) Model class to pick (don’t overthink names)

  • 7–8B “instruct/chat” model
  • 4‑bit quant (often tagged as Q4 / int4 / q4_K_M depending on the library entry)
  • Keep context at ~4096 to stay stable on 4 GB VRAM

If that’s too slow or keeps spilling to CPU too much:

  • drop to a 3–4B instruct model (also 4‑bit)

2) Ollama settings that usually improve “accuracy” (less randomness)

Ollama lets you bake these into a custom model via a Modelfile (documented here: https://docs.ollama.com/modelfile):

FROM <your-base-model>
# 
PARAMETER temperature 0.2
PARAMETER top_p 0.9
PARAMETER top_k 40
PARAMETER repeat_penalty 1.1
PARAMETER num_ctx 4096

Then:

  • ollama create my-accurate -f Modelfile
  • ollama run my-accurate

(You can also inspect a model’s template/settings with ollama show --modelfile <model>.)

3) About “thinking” variants (like your lfm2.5 thinking)

They often:

  • run slower
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  • use more tokens
  • feel “smarter” on hard questions, but can be painful on a 4 GB GPU

So I’d start with the regular instruct variant first, get it stable, then try “thinking” after.

If you tell me which exact model entry you pulled in Ollama (just the ollama list name/tag) and what context length you want (4k vs 8k), I can sanity-check whether it’s a good fit for 4 GB VRAM and what to tweak first.

slate lynx
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Can’t you suggest which model should i pull from ollama? It will save my time

spice summitBOT
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I can’t responsibly tell you “pull this exact Ollama tag” because the Ollama library/tags shift fast and I will give you a stale/wrong string sooner or later. What I can do (and it’s basically a 2‑minute path) is point you at the right 2–3 model families + the exact variant to choose for your 3050 4 GB.

Fast pick for your hardware (accuracy-first, still usable)

On Ollama, choose:

  • 7–8B Instruct model
  • 4‑bit (Q4 / int4) quant
  • Context 4096

Good families to start from (pick the 7–8B instruct Q4 variant on their pages):

If 7–8B is too slow on 4 GB VRAM

Drop to a 3–4B instruct Q4 model in the same families.

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Settings to keep it “accurate”

Make a custom Modelfile with low temperature + num_ctx 4096 (Ollama docs: https://docs.ollama.com/modelfile).

If you want, paste your current ollama list output (just the model names), and I’ll tell you which one of your installed ones is the best fit + what to tweak.

slate lynx
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Got it thanksđź‘Ťđź‘Ť

spice summitBOT
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Glad it helped.

If you end up testing a couple: start with a 7–8B instruct Q4 @ 4k ctx, and if it feels sluggish on the 3050 4 GB, drop to a 3–4B instruct Q4.

If this fixed it, please right‑click the solution message → Apps → ✅ Mark Solution so Answer Overflow indexes it for the next person.

zinc notch
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No small model will do this reliably. You can try glm-4.7-Flash:Q4_K_M but it will take minutes to reply. I stopped using qwen3:8b and qwen2.5-coder:7b despite their speed on 8gb vram. Is privacy or cost your concern?

spice summitBOT
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Yeah, that matches what we’ve been seeing too: small/local models can be “fast enough” for chat, but reliable tool-calling (clean JSON, correct args, consistent follow-through) is the part that tends to fall apart — and on 4 GB VRAM you also get a lot of CPU offload, which is where the “minutes per reply” comes from.

Tanmay — for fully offline you basically have to pick which pain you prefer:

  • Usable speed: go 3–4B instruct, Q4, keep num_ctx ~4096, avoid “thinking” variants.
  • Better accuracy: go 7–8B instruct, Q4, still num_ctx ~4096, and accept that some prompts/tool steps may need retries / more guidance.

Also +1 to what absol asked: is your main driver privacy or cost? (The best “offline compromise” depends a lot on that.)

slate lynx
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I have one another system. 32gb ram, 50708gb ,i7 core ultra 255hx. What are the recommendations for that system from your side?

spice summitBOT
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For OpenClaw specifically, the main decision isn’t “smallest model that runs” — it’s “model that can reliably do tool/function calling.” Most small local LLMs (especially when heavily quantized) tend to be flaky at tool use, so if you want OpenClaw to actually control apps/browser/files consistently, a cloud model is usually the smooth path, and you can still keep a local model around for cheap “chatty” stuff.

That said, for your second system (32 GB RAM + “5070 8GB?” + Core Ultra 255HX):

If that’s an RTX 5070 with 8 GB VRAM

  • Sweet spot: ~8B–12B class instruct models in 4-bit quantization.
  • Expectations: good general chat + light reasoning; agentic/tool reliability varies by model, so be ready to swap to cloud for “real work.”
  • Context size: keep it moderate if you see VRAM pressure (bigger context = more memory).

If you meant something else by “5070 8gb” (or no dGPU)

  • CPU-only: you’ll want to stay closer to 3B–8B class models (still quantized), and expect slower responses.

Practical recommendation (OpenClaw workflow)

  • Use cloud for the “agent brain” (tools, browsing, planning).
  • Use local for low-cost conversational tasks or experiments.
  • If you want concrete “which exact model names are best this week,” ask in #models (stuff changes fast and I don’t want to mislead you). Docs: https://docs.openclaw.ai/concepts/models
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If you confirm what “5070 8gb” is exactly (GPU model + VRAM) and what you want to do with OpenClaw (tool-heavy automation vs just chatting), I can narrow it to a recommended model size + quant + serving approach for that box.

slate lynx
spice summitBOT
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If cost is the main concern, you’ve basically got three workable strategies:

1) Go fully local (lowest $ spend, highest “time spend”)

  • Run a small-ish local instruct model that fits your GPU (quantized).
  • Tradeoffs: slower + tool/function calling can be unreliable, so OpenClaw automations may feel flaky unless you experiment a lot.

Rule of thumb for your hardware:

  • 3050 4GB VRAM → usually 3B–7B class models (4-bit), modest context.
  • 8GB VRAM GPU → usually 7B–12B class models (4-bit), still keep context reasonable.

2) Hybrid (best cost/stability ratio for OpenClaw)

  • Use local for “chat / brainstorming / simple Q&A”.
  • Use a cheap cloud model when you need OpenClaw to do tool-heavy work (browser control, file ops, multi-step planning).
    This keeps bills low and avoids the “local model got confused and stopped using tools” problem.

3) Cloud, but optimize for cost

  • Pick one of the cheapest tool-reliable models as your default, and only “upgrade” to a stronger one when needed.
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  • Biggest cost levers: shorter context, lower max output tokens, and not running long agent loops.

For the “which exact cheapest models right now?” bit: that changes fast, so the best place is #models + the models doc: https://docs.openclaw.ai/concepts/models (the community keeps the current cost/performance picks up to date).

If you tell me what you want OpenClaw to do (mostly chatting vs automating apps/browser), I can tell you whether you should bother with fully local or go hybrid to keep costs down without wasting time.

zinc notch
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Been enjoying Gemini 3-flash-preview and 2.5-flash via google api key. Lots of credits for up to 90 days free. You can get other api keys free on nvidia's site. Check #old-models for tips on new trials coming out like pony-alpha. No cost on some of them