This week, JetBrains did something we think the IDE industry should be watching: they packed a 27B-parameter Qwen-series open-source LLM directly into their IDE, enabling code completion and chat to run locally—no cloud required. What we find even more notable: they didn't pick the latest version, but rather a slightly older one that's better at "thinking."
What this is
JetBrains is the parent company of IntelliJ IDEA, PyCharm, and GoLand—one of the de facto giants of the global IDE market. Over the past few years, their main AI moves have been cloud-based completion partnerships with OpenAI and Anthropic. This is the first time they've made a clear, public bet on "running locally."
Local AI means inference happens on your own computer rather than being shipped to the cloud. For developers, this delivers two direct benefits: code doesn't need to leave the machine (compliance- and privacy-friendly), and there's no per-token billing (predictable costs). The trade-off is hardware—a 27B model generally needs 16–24GB of VRAM to run smoothly.
Qwen is Alibaba's DAMO Academy series of open-source large models, consistently among the top downloads on Hugging Face throughout 2024–2025. This time, JetBrains chose a 27B-class version from the Qwen family rather than a newer release. The consensus we see in the Reddit r/LocalLLaMA community: the newer version scores higher on standard benchmarks, but its "thinking" (i.e., reasoning and planning capability) is actually weaker than the older one—a critical factor for multi-step tasks inside an IDE.
How the industry sees it
The positive take: open-source model quality has cleared the "runs locally and is good enough" bar, and enterprise-grade tools are starting to commit to deep integration. Chinese models' presence in overseas developer circles is visibly rising—the fact that a hardcore local-AI community is seriously debating which Qwen version to pick is itself a signal we shouldn't ignore.
The pushback and risks: a local 27B has steep hardware requirements—standard developer ultrabooks can't run it, meaning this is currently a privilege for high-end workstations or gaming laptops. Additionally, JetBrains' existing product line still revolves around the cloud-based AI Assistant; how the local version will be positioned, whether it will be paid, and how it coexists with the cloud version remain unclear from official channels. There's also a cooler view we share: local AI fits hobbyists and privacy-sensitive scenarios, but cloud models' iteration speed and economies of scale remain beyond what a single developer machine can match.
Impact on regular people
For enterprise IT: industries like finance, healthcare, and government have strict requirements around code and data leaving their perimeter. Local AI is a hard requirement for them, and JetBrains' official endorsement will accelerate compliance-driven procurement decisions.
For working professionals: developers no longer need to worry about their code being sent to third-party servers—reducing the psychological burden when working remotely, taking on freelance projects, or writing sensitive business logic.
For the consumer market: "can run large models" will gradually become a selling point for high-end laptops. Configurations with 32GB of RAM and a discrete GPU may spread from the "gaming laptop" category into a new "AI workstation" tier.