LFM2-VL 3B on live video streams

What LFM2-VL 3B is good at

LFM2-VL is tuned for edge latency first: it encodes images at their native resolution rather than resizing to a fixed grid, which keeps small text and fine detail readable without a heavier encoder. That makes it a good fit for lightweight visual question answering and captioning where speed matters as much as accuracy.

As part of Liquid AI's foundation model family, LFM2-VL benefits from an architecture built for efficient inference on constrained hardware, which translates into low per-request latency even at moderate throughput.

• Native-resolution image encoding without a heavy downsampling step
• Fast visual question answering and captioning on modest hardware
• Latency-sensitive deployments where inference speed is the priority

LFM2-VL 3B and Overshoot's streaming workflow

LFM2-VL 3B is not currently in Overshoot's live model catalog, so it cannot be called through the API today. For models that are live, the workflow is: publish a camera or screen share over WebRTC through LiveKit, then send a chat-completions request referencing the stream with an ovs:// URL, either the latest frame or a recent segment within the 600-second retention window. LFM2-VL's native-resolution encoding means frames would not need pre-resizing before such a request.

Catalog models stream answers back over SSE, and thread_id enables prompt caching for repeated questions against the same stream. Those platform features would pair well with LFM2-VL's already-low per-request latency for high-frequency polling use cases, if a model like it becomes available through the API.

LFM2-VL versus other edge models

LFM2-VL 3B sits alongside other small vision models such as SmolVLM2 and Gemma 4 26B-A4B, but distinguishes itself with native-resolution encoding rather than a fixed-size image pipeline. Teams choose LFM2-VL when input images vary widely in size and detail and a fixed downsampling step would lose information, while sticking with a smaller model when raw speed matters more than resolution fidelity.

Frequently asked questions

Can LFM2-VL 3B analyze live video?

LFM2-VL 3B is designed for low-latency visual understanding, which suits sampled live video frames well. It is not currently in Overshoot's live model catalog, so it cannot read Overshoot streams today. Models that are in the catalog read a live WebRTC stream referenced with an ovs:// URL and stream answers back over SSE.

Is LFM2-VL 3B open source?

LFM2-VL is released under the LFM Open License from Liquid AI, which permits broad use of the weights, including downloading and self-hosting them on your own hardware.

Is LFM2-VL 3B available on Overshoot?

Not currently. LFM2-VL 3B is not in Overshoot's live model catalog. The catalog changes over time, so check GET /v1beta/models for the up-to-date list of models you can call through the API.

What is LFM2-VL 3B best suited for?

LFM2-VL suits latency-sensitive visual question answering and captioning, particularly when input images vary in resolution and detail. It is a strong default when a task is straightforward and response speed matters more than handling complex multi-step reasoning.

Related models

• Gemma 4 26B-A4B
• PaliGemma 2 28B
• Qwen2.5-VL 7B
• Phi-3.5 Vision