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

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.

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).

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.

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.

Beats Qwen3.5-397B-A17B (a 397B MoE model) on SWE-bench Verified (77.2), SWE-bench Pro (53.5), Terminal-Bench 2.0 (59.3), and SkillsBench Avg5 (48.2). GPQA Diamond 87.8. Native multimodal with thinking preservation. r/LocalLLaMA calls it "the biggest release of the year" and "a turning point for local inference."

Similar VRAM profile to predecessor (~17–20 GB in 4-bit); very new so quantized options are still rolling out; thinking mode can be verbose on simpler tasks (toggleable). Not quite closed-model SOTA on the absolute hardest long-horizon agent runs.

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.

Five models covering phone → laptop → server. 12B Unified: encoder-free multimodal, ~7 GB VRAM with QAT, 100+ tok/s on consumer GPUs. E2B runs in 1 GB RAM on phones. E4B scores 42.5% AIME 2026 on a smartphone. 26B MoE delivers ~97% of 31B quality at a fraction of the compute. 31B ranks top-3 among open models. All Apache 2.0. All support 140+ languages.

Five models means five sets of trade-offs. Edge models sacrifice reasoning depth. The 12B needs a decent GPU. The 26B/31B need serious VRAM. No single model does everything — you pick the one that fits your hardware. Google tooling preferred for smoothest experience.