Open-Source Multimodal LLM Implementations See Rapid Growth and Integration

Sudeep Pillai, a prominent figure in the AI community, recently initiated a public discussion seeking insights into the "best open source chat completions or assistants API implementations" with a particular focus on multimodal support. Pillai specifically called out @vllm_project, @sgl_project, and @LiteLLM as current points of interest, indicating a strong community drive towards advanced, accessible AI solutions.

vLLM (Virtual Large Language Model) is actively developing its multimodal capabilities, offering experimental support for processing image data. The project's documentation outlines mechanisms for passing multimodal inputs alongside text prompts, with ongoing efforts to integrate these features with performance optimizations like chunked prefill and automatic prefix caching. Notably, vLLM has been leveraged in geospatial AI applications through its integration with TerraTorch, enabling the consumption and production of non-text data, such as with IBM Research's Prithvi-EO-2.0 model.

SGLang, another project highlighted by Pillai, positions itself as a flexible programming model designed to enhance complex prompting and control flow for large language models. While SGLang itself does not natively provide multimodal processing, it achieves this by integrating with various high-performance LLM inference engines and APIs, including vLLM, Hugging Face Transformers, OpenAI, Google, and Anthropic. This integration allows SGLang users to tap into the multimodal features of these underlying models, orchestrating advanced AI workflows.

LiteLLM stands out for its comprehensive support of vision models across a broad spectrum of commercial and open-source providers. The platform simplifies access to multimodal APIs from entities such as OpenAI, Google Gemini, Anthropic Claude, Ollama, Replicate, and Mistral. LiteLLM facilitates the input of image data via URLs or base64 encoded strings, making it a versatile tool for developers seeking to incorporate visual understanding into their AI applications.

The broader open-source landscape is experiencing a surge in multimodal AI development. Models like Meta's Llama 3.2 Vision, Google's Gemma 3, Z.ai's GLM-4.1V-Thinking, NVIDIA's NVLM 1.0, Allen Institute for AI's Molmo, Mistral's Pixtral, and Alibaba's Qwen2.5-VL are pushing the boundaries of what open-source models can achieve. These models often support various modalities, including text, images, and sometimes video, fostering community-driven innovation and offering customizable, secure, and affordable alternatives to proprietary solutions.