← Back to news9 October 2026

reViT: a single recurrent transformer block matches a full-depth encoder

A new paper shows a single Transformer block, applied recurrently, matches a full-depth vision encoder's accuracy at comparable Inference FLOPs. The key is representing the FFN at each recurrent depth as a convex combination of a small shared expert bank, with no intermediate feature distillation.

Read original article

What a token costs: five routes, the math, and the break-even point

Frontier API, subscription, inference providers, rented GPU or your own hardware: five routes to tokens, with real monthly costs, the electricity bill, and the calculated break-even point.

GPU or CPU: when the CPU is enough

Do you really need a GPU for local AI? For many use cases, no: how prefill and decode work, what matters (memory bandwidth), real numbers on a mainstream laptop, the cases where the CPU is enough and where VRAM is needed — with the commands to measure on your own hardware.

Mac Studio M5 Ultra: 512GB of unified memory on the desktop

Four dies, 512GB of unified memory at 1.2 TB/s: the Mac Studio M5 Ultra runs 100-560B MoE models locally. The speed math, the cost comparison with the DGX Station, and when the Mac is the right choice.

Get the next local-AI briefing

Once a week, the top selection from iaLocale: articles, news and resources on local AI, and more.

Newsletter language

I consent to the processing of my data to receive the iaLocale newsletter. Read the Privacy Policy.

After signing up you receive a confirmation email (double opt-in). Without confirmation we send nothing. No spam, we promise.

Related resources

Tools and resources from the iaLocale archive relevant to this content.

⚡

Magnitude

Inference

Open-source inference engine (Rust, YC S25) that compiles and tunes its kernels directly on the target hardware: Apple Silicon, NVIDIA, AMD or CPU. On open models it claims up to 2× the speed of llama.cpp. Also aimed at serving agents.

🖥️