FastVLM 7B on real-time video

What FastVLM 7B is good at

FastVLM’s headline contribution is its encoder, not its language backbone: FastViTHD produces vision tokens fast enough that time-to-first-token drops by up to 85x versus comparable setups at high input resolution. That makes it well suited to workloads where the image is large or detailed and latency to the first token matters.

Because encoding is cheap, FastVLM can afford higher-resolution inputs than many peer models at similar latency budgets, which helps with small text, fine object detail, and cluttered scenes. It handles general visual question answering, captioning, and document-style images.

• Fast time-to-first-token on high-resolution images
• Detail-sensitive tasks: small text, fine object detail, cluttered scenes
• General visual question answering and captioning

FastVLM 7B and Overshoot's live video API

FastVLM 7B is not currently in Overshoot's live model catalog. The catalog changes over time, and GET /v1beta/models is the authoritative list, so check there for the models that are available right now.

For models that are in the catalog, the request shape is the same across the API: publish a camera or screen share over WebRTC through LiveKit, then send a chat-completions request whose image_url is an ovs:// reference to the latest frame or a specific timestamp within the 600-second retained history. Responses stream token by token over SSE, and thread_id keeps prompt caching warm for repeated questions against the same stream. An encoder as fast as FastVLM's would be most noticeable on the first response in a conversation, before caching benefits apply.

FastVLM within Apple’s research lineup and against peers

FastVLM is Apple’s answer to the common trade-off between input resolution and encoding latency: rather than downsampling images to stay fast, it redesigns the encoder itself. Compared with encoder-heavy peers like Qwen2.5-VL 7B or Gemma 3 27B, FastVLM prioritizes raw time-to-first-token over the largest possible parameter count, making it a strong fit when speed at high resolution matters more than squeezing out the last point of accuracy.

Frequently asked questions

Can FastVLM 7B analyze live video?

FastVLM 7B can process frames sampled from live video, and its fast, high-resolution encoder suits exactly that kind of workload. It is not currently in Overshoot's live model catalog, though; the catalog changes over time, so check GET /v1beta/models for the models that can be pointed at a live WebRTC stream today.

Is FastVLM 7B open source?

FastVLM 7B ships under Apple’s ML Research license, which allows research and evaluation use with some restrictions on commercial deployment. Check the license text for your specific use case before shipping it in a commercial product.

How fast is FastVLM 7B?

FastVLM's FastViTHD encoder cuts time-to-first-token by up to 85x versus comparable setups at high resolution. That means high-resolution frames do not carry the encoding latency penalty they would with a conventional vision encoder, which is what makes the model interesting for live-video workloads.

What is FastVLM 7B designed for?

FastVLM was designed for on-device, high-resolution vision, where encoding speed at full image detail matters more than raw model size. It fits use cases like reading small text, inspecting fine detail, or processing dense scenes without downsampling first.

Related models

• PaliGemma 2 28B
• Gemma 3 27B
• Qwen2.5-VL 7B
• Phi-3.5 Vision