‘Tis the week for small AI models, it seems.
On Thursday, Ai2, the nonprofit AI research institute, released Olmo 2 1B, a 1-billion-parameter model that Ai2 claims beats similarly-sized models from Google, Meta, and Alibaba on several benchmarks. Parameters, sometimes referred to as weights, are the internal components of a model that guide its behavior.
Olmo 2 1B is available under a permissive Apache 2.0 license on the AI dev platform Hugging Face. Unlike most models, Olmo 2 1B can be replicated from scratch; Ai2 has provided the code and data sets (Olmo-mix-1124, Dolmino-mix-1124) used to develop it.
Small models might not be as capable as their behemoth counterparts, but importantly, they don’t require beefy hardware to run. That makes them much more accessible for developers and hobbyists contending with the limitations of lower-end and consumer machines.
There’s been a raft of small model launches over the past few days, from Microsoft’s Phi 4 reasoning family to Qwen’s 2.5 Omni 3B. Most of these — and Olmo 2 1B — can easily run on a modern laptop or even a mobile device.
Ai2 says that Olmo 2 1B was trained on a data set of 4 trillion tokens from publicly available, AI-generated, and manually created sources. Tokens are the raw bits of data models ingest and generate — 1 million tokens is equivalent to about 750,000 words.
On a benchmark measuring arithmetic reasoning, GSM8K, Olmo 2 1B scores better than Google’s Gemma 3 1B, Meta’s Llama 3.2 1B, and Alibaba’s Qwen 2.5 1.5B. Olmo 2 1B also eclipses the performance of those three models on TruthfulQA, a test for evaluating factual accuracy.
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Ai2 warns that that Olmo 2 1B carries risks, however. Like all AI models, it can produce “problematic outputs” including harmful and “sensitive” content, the organization says, as well as factually inaccurate statements. For these reasons, Ai2 recommends against deploying Olmo 2 1B in commercial settings.
