On-device vision-language model for real-time image understanding across every platform
picoVLM delivers Qwen3-VL accuracy and capability entirely on the device across platforms. Understand images, answer visual questions, describe scenes, and read text in images. Lightweight production-grade visual understanding with no GPU dependency.
Only production-ready on-device vision-language model
picoVLM processes images and text simultaneously entirely on the local device — no cloud API call, no image upload, no per-query cost. A compact neural model handles visual encoding, multimodal reasoning, and language generation in a single pass, returning structured answers, descriptions, or extracted data.
Existing vision-language models require GPU infrastructure and transmit visual data to third-party servers. Smaller open-source alternatives sacrifice accuracy to fit on the device but cannot match the capability of production-grade cloud VLMs. picoVLM is the first to deliver Qwen3-VL-level accuracy and the full VLM capability set on CPU, across mobile, embedded, and browser environments, with no server dependency and no engineering overhead.
picoVLM handles images, documents, charts, screenshots, scientific diagrams, and handwritten notes. The model answers visual questions, describes scenes, reads text in images, interprets charts and formulas, localizes objects, and performs multi-step visual reasoning without preprocessing or fine-tuning.
Why enterprises choose picoVLM On-device Vision-Language Model
picoVLM is the only production-grade on-device vision-language model that delivers Qwen3-VL accuracy across every platform: mobile, web, embedded, and desktop. It brings the full capability set of state-of-the-art VLMs to any environment, even where GPU infrastructure is not available.
Ship it.
On device.
Accurate, lightweight, and private on-device vision-language AI
Common questions about on-device vision-language models
picoVLM On-device Vision-Language Model delivers the full capability set of Qwen3-VL entirely on device. It answers questions about images and video, describes visual scenes, reads text in images, interprets charts and documents, solves mathematical problems presented as images, and performs multi-step visual reasoning, all without sending data to a server.
picoVLM On-device Vision-Language Model matches Qwen3-VL accuracy and capability on MMMU and MMBench benchmarks while running entirely on device, including on a Raspberry Pi. Qwen3-VL requires GPU infrastructure and cannot run on mobile or embedded devices without a server. picoVLM requires no GPU and no network connection.
Yes. picoVLM On-device Vision-Language Model runs entirely on the device. It can work in air-gapped environments, on devices with no connectivity, and in areas with poor signal, with no degradation in output quality.
picoVLM On-device Vision-Language Model handles natural scene images, documents, charts, screenshots, product images, forms, handwritten notes and scientific diagrams.
Yes. picoVLM On-device Vision-Language Model supports multi-turn interactions about an image, maintaining context across follow-up questions without re-processing the visual input on each turn.
Yes. picoVLM On-device Vision-Language Model interprets equations, diagrams, and mathematical content presented as images, answering questions from scientific documents, engineering drawings, and academic content.
Yes. picoVLM On-device Vision-Language Model processes images and prompts on device with low, predictable latency, enabling real-time visual assistant and inspection experiences without network delays.
picoVLM On-device Vision-Language Model returns natural language text, structured JSON, or formatted data depending on the prompt, ready for downstream use without post-processing.
Yes. picoVLM On-device Vision-Language Model identifies specific regions of an image corresponding to described objects, returning bounding box coordinates and JSON-formatted attributes for downstream use.
No. picoVLM On-device Vision-Language Model runs on standard CPU hardware with no dedicated AI accelerator, discrete GPU, or neural processing unit required, making it deployable on commodity mobile hardware, single-board computers, and embedded devices including Raspberry Pi.
picoVLM On-device Vision-Language Model uses WebAssembly to run entirely within the browser, across Chrome, Firefox, Safari, and Edge. No server connection is required, and no data leaves the device.
picoVLM On-device Vision-Language Model runs on Android, iOS, macOS, Windows, Linux, Raspberry Pi, and all major web browsers via WebAssembly, across AMD, Intel, NVIDIA, and Qualcomm hardware.
No. picoVLM On-device Vision-Language Model runs entirely on device. Images, video, prompts, and model outputs are never transmitted to Picovoice or any third-party server. There is no data controller relationship and no processing agreements required.
picoVLM On-device Vision-Language Model is private by architecture: all processing happens locally with no data transmitted to Picovoice or any third-party server. Images, video, and outputs never leave the device. This makes picoVLM On-device Vision-Language Model intrinsically suitable for regulated environments including HIPAA (US healthcare), GDPR (EU personal data), CCPA (California consumer privacy), PIPEDA (Canada), CJIS (US law enforcement), and FERPA (US education). Picovoice does not store, process, or have access to any end-user image, video, or prompt data under any circumstances.
Yes. picoVLM On-device Vision-Language Model requires no network connectivity at any point and runs in fully air-gapped environments with no degradation in output quality.
Picovoice docs, blog, Medium posts, and GitHub are great resources to learn about voice AI, Picovoice technology, and how to start building vision products. Enterprise customers get dedicated support specific to their applications from Picovoice Product & Engineering teams. Reach out to your Picovoice contact or talk to sales to discuss support options.