Open-source AI inference engine llama.cpp is redefining the "local large model" experience with a historic update. Known for its minimal C++ code, it now features a modern web interface and has made three major breakthroughs: multimodal input, structured output, and parallel interaction, directly addressing the limitations of encapsulated tools like Ollama. This community-driven localization revolution is moving llama.cpp from a developer-only low-level engine to a versatile AI workbench accessible to ordinary users.

Multimodal capabilities fully implemented: one-click parsing of images, audio, and PDFs

The most notable feature of this update is the native integration of multimodal capabilities. Users can now directly drag and drop images, audio files, or PDF documents, combining them with text prompts to trigger cross-modal understanding. For example, uploading a technical white paper with charts will automatically convert it into an image input (if the model supports vision), avoiding formatting errors and information loss that occur in traditional OCR text extraction. Video support is already in the planning stage. This means that llama.cpp has evolved from a pure text inference tool into a local multimedia AI hub covering document analysis, creative assistance, and educational research scenarios.

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Radical improvement in interaction experience: parallel chat, Prompt editing, mobile-friendly

The new web interface is built on SvelteKit, lightweight and responsive, and perfectly adapted to mobile devices. Users can open multiple chat windows simultaneously, handling image analysis while performing code generation; they can also modify any prompt in the history and regenerate responses, easily exploring different answer branches. Through the --parallel N or --kv-unified parameters of llama-server, the system can intelligently allocate VRAM and context, achieving efficient resource utilization. Sessions support one-click import and export, ensuring privacy while maintaining cloud-level convenience.

Innovative features boost efficiency: URL direct connection and JSON structured output

Two hidden gems reflect developers' ingenuity:

First, URL parameter injection — users simply append text parameters to the browser address bar (e.g., ?prompt=explain quantum computing), and a conversation will be automatically started. Chrome users can even trigger analysis with a single click after simple configuration, greatly simplifying repetitive query processes.

Second, custom JSON Schema output — after defining a structure template in settings, the model will strictly generate results according to the specified format, eliminating the need for repeated prompts such as "Please return in JSON." Tasks such as invoice information extraction, data cleaning, and API response generation can now achieve "template as a service," truly advancing toward enterprise-level automation.

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Performance and privacy guaranteed, open-source ecosystem sets a new benchmark

The update also includes several professional optimizations: inline rendering of LaTeX formulas, real-time preview of HTML/JS code, fine-tuning of sampling parameters (Top-K, Temperature, etc.), and improvements in context management for models like Mamba, significantly reducing computational costs during multi-task concurrency. Most importantly, all operations run 100% locally, without relying on the cloud or uploading data. In an era where AI privacy concerns are increasing, it provides a truly trustworthy local intelligent solution.

AIbase believes that this upgrade of llama.cpp has gone beyond the scope of an "inference engine," and is building an open, efficient, and secure local AI ecosystem standard. Facing competitors like Ollama that only do simple packaging, llama.cpp shows a "downward strike" advantage with its deep integration, flexible expansion, and community-driven strengths. As more developers join in building together, this local AI revolution ignited by C++ code may reshape the future landscape of large model applications.