End-to-end omni-modal architecture
Built as an end-to-end omni-modal model on SigLip2, Whisper-medium, CosyVoice2, and Qwen3-8B, with 9B parameters.
MiniCPM-o 4.5
MiniCPM-o 4.5 is a multimodal AI model on Hugging Face for vision, speech, text, and full-duplex live streaming. It supports local and server-side inference paths, including PyTorch, llama.cpp, Ollama, vLLM, SGLang, and quantized formats.
Overview
MiniCPM-o 4.5 is a multimodal model on Hugging Face from openbmb that is built for vision, speech, text, and full-duplex live streaming on phones and local devices. The model page describes it as the latest and most capable model in the MiniCPM-o series, with 9B parameters and an end-to-end architecture built on SigLip2, Whisper-medium, CosyVoice2, and Qwen3-8B.
Its capabilities are centered on real-time interaction: it can handle continuous audio and video streams, generate text and speech concurrently, and support proactive responses during a live scene. The page also highlights strong OCR and document parsing performance, bilingual speech conversation, configurable voices, voice cloning from reference audio, and several inference paths for local and high-throughput deployment.
Features
Real-time live streaming
Supports full-duplex multimodal live streaming, generating text and speech while consuming continuous audio and video streams without mutual blocking.
Speech conversation and voice control
Handles bilingual speech conversation in English and Chinese with configurable voices, plus voice cloning and role play from a short reference clip.
Instruct and thinking modes
Supports both instruct and thinking modes in a single model, letting users choose between efficiency-oriented and reasoning-oriented interaction styles.
High-resolution vision and multilingual support
Processes high-resolution images up to 1.8 million pixels and high-FPS video up to 10 fps, with multilingual capability across more than 30 languages.
Flexible inference and serving options
Offers multiple deployment paths, including PyTorch on Nvidia GPU, llama.cpp, Ollama, int4 and GGUF quantized models, vLLM, SGLang, and FlagOS.
Use Cases
Real-time multimodal assistants
Build assistants that can watch a live scene, listen to incoming audio, and speak back without waiting for one modality to finish before another starts.
On-device or local demos
Run local demonstrations on a phone, Mac, or GPU-enabled device using the released web demos or supported CPU-friendly runtimes.
Speech interaction and voice cloning
Create speech applications that need bilingual conversation, configurable voices, or voice cloning from a short reference recording.
Document and OCR workflows
Extract text from complex images or documents and work with OCR-heavy workflows that benefit from support for high-resolution inputs.
High-throughput serving
Serve model responses at higher throughput with vLLM or SGLang when a project needs more efficient batch or production-style inference.
Pros and Cons
Pros
- Combines vision, speech, text, and full-duplex streaming in one model.
- Supports both instruct and thinking modes within the same model.
- Offers local and serving-oriented options, including llama.cpp, Ollama, vLLM, SGLang, and quantized formats.
- Includes bilingual speech features, configurable voices, and reference-audio voice cloning.
- Handles high-resolution images and high-FPS video while also supporting more than 30 languages.
Cons
- The source does not provide clear pricing or access terms for inference on the model page.
- Several capability claims are benchmark-based and should be evaluated in context for a specific workload.
- The most complete setup is described as PyTorch inference with an Nvidia GPU, so lighter local setups may involve trade-offs.
FAQ
What is MiniCPM-o 4.5 used for?
MiniCPM-o 4.5 is presented as a multimodal model for vision, speech, and full-duplex live streaming. The page also notes support for traditional text and vision-language requests through its API service.
How can MiniCPM-o 4.5 be run or deployed?
The page describes PyTorch inference with Nvidia GPU as the basic usage recommended for full precision. It also lists llama.cpp and Ollama for local CPU inference, quantized int4 and GGUF models, vLLM and SGLang for higher-throughput serving, and FlagOS for multi-chip backends.
What kinds of inputs and outputs does it support?
The source says the model supports bilingual real-time speech conversation in English and Chinese, and it can handle images, video, audio, text, and multimodal live streams.
What are the model’s main content and language capabilities?
The page says the model can process high-resolution images up to 1.8 million pixels, high-FPS video up to 10 fps, and supports more than 30 languages.
What makes MiniCPM-o 4.5 different from a standard multimodal model?
The source highlights a full-duplex multimodal live streaming mechanism and proactive interaction, where the model can decide at 1 Hz whether to speak based on the live scene. It is described as useful for fluid real-time omnimodal conversation.
Quick Facts
- Platform
- Hugging Face
- Model repo
- openbmb/MiniCPM-o-4_5
- Category
- Multimodal AI model
- Primary modalities
- Text, vision, speech, audio, video
- Source domain
- huggingface.co
- Deployment options
- PyTorch, llama.cpp, Ollama, vLLM, SGLang, FlagOS
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