Rankings come from what real users say about AI models on public forums, not from benchmarks. Refreshed every month.

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Best local AI model to run on your own computer (October 2026)

Updated Oct 29, 2026 · Based on public comments from Aug 29, 2026 to Oct 28, 2026

Local models run on your own GPU or Mac with no subscription. We counted opinions from people who run open-weight models themselves, mostly in r/LocalLLaMA.

Best for coding and agents: Qwen3.8 (27B, and Flash Next if you have the memory).

Best for chat, writing and languages: Gemma 4 (31B or 26B A4B).

Big-hardware pick: DeepSeek V4.1 Flash and GLM 5.3 Flash, if you can run them at a good quantization.

The ranking

Score = share of positive opinions, smoothed toward 50 for small samples.positivemixednegative

  1. #1Qwen3.8Alibaba80/100 · 80% positive · 15 opinions

    The most recommended local model for coding, agents and logic. Some find dense versions slow and prefer Gemma's writing style.

    Sources: praise in r/LocalLLaMA · praise in r/LocalLLaMA · mixed in r/LocalLLaMA

  2. #2Gemma 4Google76/100 · 75% positive · 20 opinions

    Loved for conversation, creativity, general knowledge and languages. Weak at tool calling and sometimes confidently wrong on vision tasks.

    Sources: praise in r/LocalLLaMA · praise in r/LocalLLaMA · complaint in r/LocalLLaMA

  3. #3DeepSeek V4.1DeepSeek73/100 · 83% positive · 6 opinions (small sample)

    Strong if you have the hardware, and some run small quants on modest GPUs. One tester found it degrades more than Qwen at low-bit quants.

    Sources: praise in r/LocalLLaMA · praise in r/LocalLLaMA · mixed in r/LocalLLaMA

  4. #4GLM 5.3Zhipu AI66/100 · 64% positive · 11 opinions (small sample)

    A favorite on multi-GPU workstations for agentic work. Some saw it loop on tasks or found Qwen Flash Next just as good.

    Sources: praise in r/LocalLLaMA · praise in r/LocalLLaMA · complaint in r/LocalLLaMA

How this ranking works

We read public comments about AI models posted between Aug 29, 2026 and Oct 28, 2026, kept the ones that judged a specific model version on this task, and labeled each as positive, mixed or negative. Each person counts at most a few times, and bots and off-topic mentions are left out. Rankings show what users report, not lab benchmarks, and a loud minority can sway small samples. Read the full method.

This page is refreshed every month. Model names change fast, so always check which version a review is about.

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