US What a multimodal LLM is
A multimodal LLM (MLLM) is a large language model that can take in more than one kind of data. A standard LLM reads and writes text. A multimodal LLM keeps that language core but adds the ability to accept images, video frames, audio or a mix of these in the same prompt, and to reason across them. Ask it “does the noise in this recording match the wear in this photo?” and it can consider both.
Well-known examples include OpenAI’s GPT-4o, which handles text, images and audio; Google’s Gemini models, which Google describes as natively multimodal; Meta’s Llama 3.2 Vision for text and images; and open models such as Alibaba’s Qwen2.5-Omni and Microsoft’s Phi-4-multimodal, which add speech input. Some multimodal LLMs can also generate speech, and a few generate images, but text remains the most common output.
Inputs a multimodal LLM can handle
Each new input type needs its own encoder to turn raw data into something the language model can read. The model’s abilities depend on which encoders it was trained with.
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Text. The prompt, instructions, documents and conversation history, as in any LLM.
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Images. Photos, screenshots, diagrams and scanned pages, encoded by a vision encoder. Models that cover text and images only are usually called vision language models.
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Video. Handled as a sequence of sampled frames, sometimes with timestamps, so the model can describe what changed over a clip.
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Audio and speech. Spoken questions, alarms and machine sounds, encoded by an audio model similar to those used for speech recognition.
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Other sensor data. Depth, lidar and time-series signals are active research areas. Most production systems still convert them to images or text first.
How multimodal LLMs are built
There are three common designs, plus a pipeline approach that behaves similarly but is not a single model.
| Design | How it works | Examples | Trade-off |
|---|---|---|---|
| Encoder and projector | A modality encoder feeds tokens into a pretrained LLM through a small projection layer | LLaVA, VILA | Cheap to build; reuses an existing LLM |
| Cross-attention adapters | Extra attention layers inside the LLM look at the encoder’s features | Flamingo, Llama 3.2 Vision | Keeps the text model intact; more layers to train |
| Natively multimodal | Trained on mixed text, image and audio data from the start | Gemini, GPT-4o | Strong cross-modal reasoning; very costly to train |
| Chained pipeline | Separate speech recognition, LLM and text-to-speech models in sequence | Whisper + LLM + TTS | Simple to deploy on the edge; adds latency between stages |
Training a multimodal LLM normally runs in stages: align each encoder with the language model on paired data, then instruction-tune the whole model on mixed tasks so that it follows prompts across modalities.
Multimodal LLM vs vision language model
Every vision language model is a multimodal LLM, but not every multimodal LLM is a vision language model. VLMs cover the most common pairing, images and text. The wider multimodal LLM family adds audio, video understanding over time and, in some models, spoken output.
The distinction matters when choosing a model for a machine. If operators will type or tap, a VLM is enough. If they will speak to the system with gloves on, or the system has to listen for abnormal sounds, you need audio as well, either from a natively multimodal model or from a speech front end in a pipeline.
Where multimodal LLMs fit in industrial systems
The strongest cases combine what a machine sees, what it hears and what a person asks.
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Hands-free operator assistants. A worker asks a question out loud at an edge AI panel PC and the system answers using the camera view, the manual and the machine’s status.
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Maintenance support. A technician photographs a fault, describes the symptom and gets likely causes and the relevant procedure.
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Audio-visual monitoring. Video and sound are assessed together, so an alert can say what happened, not only that a threshold was crossed.
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Robotics and vehicles. Spoken commands are grounded in camera input, and the machine can explain what it is doing.
Running a multimodal LLM on edge hardware
Running a multimodal LLM locally keeps audio and images on site and removes network delay. The cost is memory: the language model, each encoder and the growing context all have to fit. Quantising to 8-bit or 4-bit weights helps, usually with a small loss in quality, and many teams pair a mid-sized LLM with separate, efficient speech and vision models rather than one very large model.
Jetson AGX Orin computers with 32 or 64 GB of memory can host smaller multimodal models plus speech recognition. For larger models, more users or more streams, industrial GPU computers take full-size NVIDIA cards. Our guide to how edge AI works covers the wider trade-offs of running models on site.
Quick answers on multimodal LLMs
Multimodal LLMs: common questions
What does multimodal mean in AI?
More than one type of data. A multimodal AI model takes in, and sometimes produces, more than one kind of data, such as text, images, audio and video.
When is an AI model considered multimodal?
When it accepts more than one modality. A model that only reads text is unimodal. One that can take an image or audio alongside text in the same request is multimodal.
Is GPT-4o a multimodal model?
Yes. OpenAI built GPT-4o to accept text, images and audio and to respond in text or speech, which makes it one of the best-known multimodal LLMs.
What is the difference between an LLM and a multimodal LLM?
Inputs. A standard LLM works with text only. A multimodal LLM adds encoders for images, audio or video so it can reason over them as well.
Can a multimodal LLM run offline?
Yes, with open models. Open-weight multimodal models can run entirely on local hardware with no internet connection, provided the system has enough memory and compute for the model size you choose.
We supply Jetson and GPU edge AI computers for on-site LLM projects with the module or GPU, memory, storage and operating system image fitted and tested as standard. Tell us the model, the inputs and how many users it serves, and we will size the system.