Ranked guide

Local / Private AI — Your Brain, Your Machine, Your Rules

Here's a radical idea: what if you could run a genuinely smart AI on your own hardware, and nothing you told it would ever leave your machine? No cloud servers. No data collection. No subscription fees. Just you, your laptop, and an intelligence that respects your privacy by design. Welcome to the open-weight revolution.

Decision first

Our ranking

Start with the winner, then compare the trade-offs that might change the answer for you.

#1 Local / Private AI

GLM-5.2

Zhipu AI

The open-weight model that rewrites the rules for local AI. Design Arena #1, SWE-bench Pro 62.1%, Terminal-Bench 82.7, AkitaOnRails 87/100 — and every bit of it available under MIT license for you to download, quantize, and run on your own hardware. A properly trained 1M context window, two reasoning effort levels, and the first open model to genuinely compete with closed frontier leaders on long-horizon engineering tasks.

Why It Wins

Strongest open model ever released for coding and agentic work — Design Arena #1 (Elo 1360), AkitaOnRails 87/100 Tier A (+41 from GLM-5.1), SWE-bench Pro 62.1% (SOTA open-weight), FrontierSWE 74.4% (1% behind Opus 4.8). MIT license with zero restrictions. 744B MoE (~40B active) — more compact than DeepSeek V4's 1.6T while delivering stronger verified benchmarks. Runs on vLLM, SGLang, ktransformers. Fits on 256GB unified memory Macs with aggressive quantization (~241GB at dynamic 2-bit).

The Catch

744B MoE still requires serious hardware — 256GB+ unified memory or multi-GPU clusters. Not a laptop model. No native vision capabilities. Slower per-token than compact models like Qwen 3.6 27B or Gemma 4. Western ecosystem tooling still maturing.

9.0 Editorial score
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Best for

The open-weight model that rewrites the rules for local AI. Design Arena #1, SWE-bench Pro 62.1%, Terminal-Bench 82.7, AkitaOnRails 87/100 — and every bit of it available under MIT license for you to download, quantize, and run on your own hardware. A properly trained 1M context window, two reasoning effort levels, and the first open model to genuinely compete with closed frontier leaders on long-horizon engineering tasks.

Why It Wins

Strongest open model ever released for coding and agentic work — Design Arena #1 (Elo 1360), AkitaOnRails 87/100 Tier A (+41 from GLM-5.1), SWE-bench Pro 62.1% (SOTA open-weight), FrontierSWE 74.4% (1% behind Opus 4.8). MIT license with zero restrictions. 744B MoE (~40B active) — more compact than DeepSeek V4's 1.6T while delivering stronger verified benchmarks. Runs on vLLM, SGLang, ktransformers. Fits on 256GB unified memory Macs with aggressive quantization (~241GB at dynamic 2-bit).

Watch out

744B MoE still requires serious hardware — 256GB+ unified memory or multi-GPU clusters. Not a laptop model. No native vision capabilities. Slower per-token than compact models like Qwen 3.6 27B or Gemma 4. Western ecosystem tooling still maturing.

#2

Kimi K3

Moonshot AI

The first open-weight model that looks like a closed frontier brain. 2.8 trillion parameters of mixture-of-experts, native vision, a full million-token context, and a license that lets you run it commercially — all downloadable to a machine you control. The catch is the machine: it needs a datacenter, not a desk.

8.5 Editorial score
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#3

Qwen3.6 — 27B

Alibaba (Qwen Team)

Alibaba's latest 27B dense model doesn't just succeed the previous local AI king — it surpasses their own 397B flagship on every major agentic coding benchmark while running on a single consumer GPU. SWE-bench Verified 77.2, Terminal-Bench 2.0 59.3, native vision and video, Apache 2.0. The local inference turning point.

8.3 Editorial score
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#4

Gemma 4

Google DeepMind

Not one model — five. Google DeepMind's Gemma 4 is a family spanning everything from a 2-billion-parameter sliver that runs on your phone to a 31-billion-parameter powerhouse for servers. Each member has different architecture, different strengths, and different hardware requirements. The E2B fits in 1 GB of RAM. The 12B Unified runs a full multimodal AI on a laptop GPU. The 26B MoE activates only 3.8B parameters per token. All Apache 2.0, all open weights. This guide walks through each one so you know exactly which Gemma fits your hardware and your workflow.

8.2 Editorial score
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