标签：#ondevice

Today SparkLLM releases and open-sources two on-device general models: Spark X2.5-4B and Spark X2.5-1.7B . Both natively support a context window of up to 1,000,000 tokens — as far as we know, the only on-device models to do so. Why 1M context on-device In real work, you rarely hand a model a single question — you hand it a whole after-sales manual, a set of meeting materials, a batch of project docs, or an entire code repository. On-device models used to chop long content into pieces and ask about each separately, which loses context and drops information. Spark X2.5-4B and 1.7B natively support up to a 1M-token context window, trained on hundreds-of-billions-of-tokens of high-quality long-document data, so they can take in and reason over far more information in a single task — and keep the full picture across a continuous, multi-step interaction. Not just answering — doing the work Long context decides whether the model can see everything; agent + tool-use decides whether it can act on it. Office (with Loomy): upload a sales spreadsheet and ask for an analysis plus a bilingual department report — X2.5-4B writes a script to aggregate the data, extracts key metrics and trends, generates a ~3,000-word Chinese report, produces an English version in the same structure, and validates content, structure and layout end to end. Code: on algorithm implementation, completion and generation, X2.5-4B rivals cloud models 2–3× its size . It plugs into open harnesses like DeepSeek Harness, OpenCode, Codex and Pi for local dev and automation — with low latency, offline use, and code kept on-device. Smart home: on the Domux smart-home test set, X2.5-1.7B reaches 90.3% end-to-end command accuracy at 0.85s average latency. Robotics: both sizes suit continuous perception-and-execution on-robot or on edge devices — operation control, target tracking, navigation decisions — with less dependence on the cloud. Domestic compute, open deployment Both models were trained end to end on a ful