PrismML’s Bonsai 8B trains a large language model with 1-bit weights from scratch, squeezing 8.2 billion parameters into just 1.15 GB. In benchmarks it ties or outperforms FP16 models like Llama 3.1 and runs at real-time speeds on phones, shifting the size-performance trade-off.

Chandra OCR 2, a 4 billion-parameter model from Datalab, outperforms GPT-4o and Gemini on AllenAI’s olmOCR benchmark and a 90-language test while halving the model size. It preserves layout, reads complex tables and math notation, converts diagrams to Mermaid, and runs at two pages per second on an NVIDIA H100. The code is Apache 2.0 but the model weights use an OpenRAIL-M license with commercial restrictions.

A new open-source OCR model outperformed all major commercial tools on standard text and handwriting tests. It accurately transcribed a 1913 handwritten letter by Ramanujan, preserving layout, math notation, and faint ink details.

The article reviews Kalshi’s inaugural research conference, showing prediction markets expanding beyond elections and sports into macro, political, and corporate hedging. It explains how direct event benchmarks simplify institutional hedging, maps the three-stage adoption process, and highlights collateral requirements and regulatory steps as key hurdles.

The article argues that enterprises should measure AI infrastructure economics by cost per token rather than raw compute metrics like FLOPS per dollar. It shows how maximizing delivered tokens—through hardware, software and system optimizations—drives down real-world cost and boosts revenue, citing NVIDIA Blackwell’s 35× lower token cost versus Hopper.