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

Alibaba's 27B hybrid monster runs on a single 24 GB GPU and genuinely competes with cloud frontier models — vision, coding, 262K context, and 201 languages, all Apache 2.0 licensed. The first local model where you stop compromising.

Benchmark-leading in its class (GPQA 85.5, SWE-Bench 72.4, LiveCodeBench 80.7). First local model with real multimodal — vision, video, OCR. Excellent agent and tool-calling. r/LocalLLaMA calls it "the new daily driver."

Needs ~17–18 GB VRAM in 4-bit — great on 24 GB cards, tight on 16 GB setups. Thinking mode on by default (easy to turn off). Not quite frontier-closed-model level on the absolute hardest multi-turn agent tasks.

Z.ai's open-weight agentic powerhouse — built to code for eight hours straight without losing the plot. Same MIT license, same open freedom, but now with sustained autonomous execution that rivals the best closed models on real-world engineering tasks.

New SOTA on SWE-Bench Pro (58.4), massive CyberGym jump to 68.7, and real-world demos of 655+ iteration coding sessions lasting 8+ hours. Runs on the same hardware as GLM-5 — swap the weights and go.

Still a very large model (~754B total params). Even with 40B active parameters per token and heavy quantization, expect high VRAM needs. Text-only — no vision or multimodal input. Thinking mode can add latency on simple queries.

Google's answer to 'what if a frontier AI ran on your phone?' Gemma 4 isn't one model — it's a family of four, from a 2-billion-parameter edge model that fits in 1.5 GB of RAM to a 31-billion-parameter dense powerhouse. The E2B and E4B variants bring multimodal intelligence — text, images, and audio — to smartphones, without an internet connection.

E4B scores 42.5% on AIME 2026, doubling the previous generation's 27B model. Full Apache 2.0 license. Native audio input on edge models. 140+ language support. Four distinct sizes covering every deployment scenario from Raspberry Pi to workstation.

Smaller edge models (E2B, E4B) lack the raw reasoning depth of desktop-class models. No video input on the edge variants (only 26B and 31B). Google ecosystem tooling preferred — less out-of-the-box compatibility with non-Google deployment stacks.