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Decart AI is a company building models and the inference infrastructure needed for live, responsive AI. Its public product structure has three layers: DOS, an inference and performance stack; Lucy, which transforms a live video stream; and Oasis, which generates an interactive world in response to user actions. The crucial distinction is that Lucy edits incoming video, while Oasis creates the next frames of a playable environment.
Decart AI in one view
Decart presents itself as both a model developer and an infrastructure company. The aim is to make generative systems respond while video or interaction is happening, rather than producing a finished clip after a long offline render.
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| Layer | What it does | Input and control | Output |
|---|---|---|---|
| DOS | Inference and performance stack for running live AI workloads | Model requests and runtime compute | Lower-latency model inference, depending on hardware and deployment |
| Lucy | Real-time video editing and transformation | Live camera or video stream, plus an edit direction or reference | Transformed video stream that changes as the source scene changes |
| Oasis | Interactive world generation | Keyboard and mouse actions, with optional visual context | New frames that form a playable, input-responsive environment |
These names describe different jobs, not interchangeable versions of one feature. Calling Oasis a video editor, or Lucy a complete game-world simulator, obscures how each system is controlled and evaluated.
What “real time” means for Decart’s systems
Lucy transforms a live video feed
Lucy starts with an existing stream. As each frame arrives, the system generates a corresponding transformed frame. Decart’s July 2026 Lucy 2.5 publication says the process responds continuously to motion, lighting and the evolving scene instead of relying on a pre-rendered clip.
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Examples described by Decart include changing clothing or characters, adding visual effects, changing a scene’s style, removing objects and transforming an environment. The edit therefore follows the camera’s movement and the subject’s changing position. It is video-to-video generation performed while the source video is still being captured or delivered.
Oasis generates what happens next
Oasis receives user actions such as keyboard and mouse input and generates the next visual frames of an environment. Decart’s October 2024 description says it internally simulates elements such as physics, rules and graphics so that the output behaves more like a playable space than a modified camera feed.
That difference changes the technical problem. Lucy must preserve enough of the incoming scene while applying an edit. Oasis must infer a plausible continuation of a generated world after every action. Neither process should be confused with playing back a fixed animation.
How Oasis generates an interactive world
Oasis provides Decart’s clearest public explanation of a real-time generative architecture. The following description applies to the Oasis publication from October 2024; it should not be treated as a disclosure of the exact design of every current Decart model.
- Compress the visual state. Instead of processing every full-resolution image directly, the system works in a compressed latent representation. This reduces the amount of data the model must manipulate for each prediction.
- Predict the diffusion process. A transformer predicts the steps of a diffusion process in that latent space. Diffusion supplies a mechanism for turning a noisy or incomplete representation into a visual frame, while the transformer supplies learned context and sequence modeling.
- Carry temporal context forward. Temporal-attention layers bring information from earlier generated frames into the current prediction. This gives the model a basis for maintaining motion and scene continuity.
- Condition on the user’s action. Keyboard or mouse input is included when predicting the next state. An action can therefore change the next generated frame instead of merely triggering a pre-authored branch.
- Decode and display the frame. The predicted latent representation is converted back into an image for display, then becomes part of the history used for subsequent predictions.
This is an autoregressive loop: the system generates one continuation, feeds its resulting state into the next decision, and repeats. Small errors can accumulate over many steps, which is one reason temporal context and fast inference are central to the experience.
How Lucy 2.5 keeps edits moving with the scene
Synthetic video training data
Decart says Lucy 2.5’s training process starts with image edits and uses specialized models to propagate those edits across video sequences. This creates examples of how a requested change should persist as a person moves, lighting changes or the camera shifts, without requiring every training sequence to be manually edited frame by frame.
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Self-anchoring for temporal stability
Lucy also describes a method called self-anchoring. After a stream starts, the model takes a snapshot of its own generated output and uses that result as a reference. The intended effect is to keep the transformation more stable over time, reducing the chance that an outfit, character identity or environmental style drifts from one moment to the next.
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For runtime performance, Decart names low-precision quantization formats including MXFP8 and NVFP4, dynamic sparse attention and deeper kernel fusion. Quantization uses fewer bits for some calculations; sparse attention limits attention work to selected relationships; kernel fusion combines operations to reduce execution overhead. These are vendor-described techniques, not independent benchmark findings.
How edit strength can be controlled
Decart’s February 2026 AdaOr publication describes a separate editing-control idea. It introduces an identity token representing “no change” and interpolates the model’s guidance origin between faithful reconstruction and a full prompt-driven edit.
In practical terms, the control is intended to vary how strongly the prompt alters the source. At one end, the output should remain close to the input; at the other, the prompt can drive a more substantial transformation. The method is designed to make that transition smoother, rather than allowing a minor wording change to produce an unpredictable jump in edit strength.
AdaOr is a published research method. The available description does not establish which production model versions expose this control, so it should not be presented as a guaranteed Lucy setting.
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Decart has published several figures, but they refer to different products and dates. Keeping their qualifications attached is essential.
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- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
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| Figure | Applies to | How to interpret it |
|---|---|---|
| 30 FPS at 1080p | Lucy 2.5, company description from 2026 | Decart’s stated capability for live transformations, not an independently validated result |
| Up to 4× speedups on compute-bound operations | Lucy 2.5, company description from 2026 | Attributed to low-precision techniques on compatible hardware; not a universal end-to-end speedup |
| 47 ms inference per frame | Oasis publication from 2024 | Historical, model-specific inference figure; not a current Lucy 2.5 latency claim |
| 360p at 20 FPS on NVIDIA H100 GPUs | Oasis demonstration from 2024 | Demo conditions for that published Oasis system, not a specification for current Lucy output |
A frame-rate claim is not the same as camera-to-screen latency. A system may generate frames quickly while still feeling slow if capture, network transfer, queuing, decoding or display adds delay.
Why end-to-end latency is the useful measure
Decart’s developer documentation warns that network round-trip time alone can understate perceived lag. Inference is a separate part of the path between a camera and the displayed transformed frame.
The meaningful measurement is often called glass-to-glass latency: the interval from the source camera’s captured image to the corresponding output appearing on the viewer’s display. Decart documents browser timing support for measuring startup and steady-state glass-to-glass latency when the required browser capabilities are available.
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A practical latency budget can include:
- camera capture and browser processing;
- upload and network transport;
- model queueing and inference;
- output transport and decoding; and
- display scheduling.
These components vary with resolution, frame rate, hardware, geographic distance and concurrent demand. Consequently, a historical Oasis inference number cannot predict the experience of a current Lucy integration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the developer SDK integration looks like
Decart’s SDK documentation illustrates a browser-based service shape rather than a complete local model installation. A camera’s MediaStream is sent to a selected real-time model, and the service returns a transformed remote stream.
- Obtain a camera stream. In a browser, request camera access and keep the resulting
MediaStreamavailable to the application. - Select the real-time model. The SDK request identifies the Decart model that should perform the transformation.
- Send the stream. The browser transmits the camera feed to the service. Network conditions now become part of the user-visible path.
- Receive the remote output. The SDK provides the transformed stream returned by inference.
- Measure the experience. Where browser support permits, measure glass-to-glass startup and steady-state latency instead of reporting network RTT as the whole result.
This architecture is useful for interactive effects, demonstrations and applications that already handle browser media. It also means developers must account for permissions, bandwidth, service availability and the difference between a model’s nominal frame rate and the latency users feel.
Known limitations and how to read them
Historical Oasis limitations
Decart’s 2024 Oasis publication acknowledges that its demonstration could produce hazy output in some situations, have difficulty recalling details from many frames earlier and produce unclear results when given an initial image outside the model’s distribution. Those are limitations disclosed for that published Oasis system. They should not automatically be assigned to Lucy 2.5 or to later Oasis releases.
Temporal consistency is a separate quality target
Generating quickly does not guarantee that an edit remains stable. A transformed face, garment or object can drift even when every frame arrives on time. Lucy’s self-anchoring approach is specifically aimed at this temporal problem, while Oasis’s temporal attention addresses the need to use earlier generated frames when continuing a world.
Vendor claims need context
Decart’s 30-FPS, 1080p and “up to 4×” figures are company-published claims. Hardware compatibility, scene complexity, prompt type, concurrency and the definition of a compute-bound operation can all affect actual results. A deployment decision should use measurements from the intended model, resolution and network path.
Lucy or Oasis: which concept matches the job?
| Question | Lucy | Oasis |
|---|---|---|
| What is being generated? | A transformed version of an incoming video | The next frames of an interactive world |
| Primary input | Camera or video stream, with an edit direction or reference | Keyboard and mouse actions, with visual context where supported |
| Main control challenge | Keep the requested edit aligned with motion, lighting and identity | Keep the world coherent as actions change the generated state |
| Most relevant quality measures | Edit fidelity, temporal stability, resolution, frame rate and end-to-end delay | Action responsiveness, visual continuity, memory over time, frame rate and end-to-end delay |
| Published performance context | 2026 company claims for Lucy 2.5 | 2024 demonstration figures for Oasis |
Use Lucy when the source video is the thing you want to alter. Use the Oasis concept when the system must invent and continue a responsive environment rather than preserve an existing camera scene.
What Decart AI is—and is not—in practical terms
Decart is not best understood as a single consumer filter or one standalone chatbot. Its public framing combines models with the systems needed to run inference quickly enough for live interaction. DOS represents that performance layer, while Lucy and Oasis demonstrate two different applications of real-time generation.
The available material supports software, model and developer-service descriptions. It does not establish a Decart-specific physical product that a reader needs to purchase. Cameras can serve as an SDK input, and GPUs can provide infrastructure, but neither is a Decart product requirement established by these descriptions.
Quick Recap
The key takeaways
- Decart’s real-time strategy has three public layers: DOS for inference performance, Lucy for live video transformation and Oasis for interactive world generation.
- Lucy generates edited frames from a running video stream; Oasis autoregressively generates what happens next after user actions.
- Oasis’s disclosed architecture combines compressed latent representations, diffusion, transformer prediction and temporal attention. That explanation is specific to the October 2024 publication.
- Lucy 2.5’s 30 FPS and 1080p figures, and its claimed compute-bound speedups, are vendor statements rather than independent benchmark results.
- The older Oasis figures of 47 ms per-frame inference and 360p at 20 FPS on H100 hardware should not be transferred to Lucy.
- For an actual integration, measure glass-to-glass latency because inference contributes to delay alongside network transport.
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