ALLaM-2-7b is a bilingual Arabic-English language model built by the National Center for Artificial Intelligence at the Saudi Data and AI Authority, designed to advance Arabic language technology while preserving strong English ability. Its originators trained it from scratch in two stages, beginning with a large English-focused pass before continuing on a mixed Arabic and English corpus, a recipe aimed at transferring knowledge across languages without losing prior capability. The result is an instruction-tuned model positioned for conversational applications in both Arabic and English contexts.

On Groq, the model is delivered with a reported throughput of roughly 1,800 tokens per second and supports structured output through JSON Object Mode, making it a practical choice for developers building bilingual assistants or retrieval pipelines that need reliable, schema-aware responses. Its autoregressive design, paired with Groq's TruePoint numerics for efficient inference, gives it a useful balance of language coverage and responsiveness for chat-oriented workloads where Arabic fluency and English competence both matter.