Developers who want to run TensorFlow Lite models in the browser with LiteRT.js often hit a few recurring problems that slow down adoption and cause bugs in production. The first issue is manual memory management. LiteRT.js does not garbage‑collect Tensor objects, so every Tensor created with new Tensor or returned from model.run must be deleted with the delete method. Forgetting to delete a tensor leads to gradual GPU or WASM memory leaks that crash the tab after a few minutes of use. The solution is to wrap every inference call in a try finally block that deletes the input tensor, all output tensors, and any intermediate tensors moved back to WASM. Writing a small helper function that takes the model and input array, runs the inference, and returns the result while handling cleanup removes the chance of omission.
A second common pain point is choosing the right accelerator. LiteRT.js supports CPU (WASM), GPU (WebGPU via ML Drift) and NPU (WebNN) but does not allow partial delegation; the whole graph must run on a single backend. If a model contains an operator unsupported by the chosen accelerator, LiteRT falls back to WASM silently, which can give unexpected latency spikes. Developers should test each backend with the official @litertjs/model‑tester package before shipping. Running npx model‑teter on the model with random inputs shows which backends succeed and which fall back, allowing the team to pick the accelerator that gives the best speed without surprises.
A third barrier is model conversion from PyTorch. LiteRT.js requires .tflite files that are exportable with torch.export.export, meaning no Python conditionals that depend on runtime tensor values and no dynamic shapes including batch size. Teams often try to convert a model that uses dynamic loops and get cryptic errors. The fix is to refactor the model to static shapes before export, or to use a fixed batch size of one and reshape inputs in JavaScript preprocessing. The AI Edge Quantizer can then be applied to reduce size and improve speed without breaking the static‑shape requirement.
Finally, preprocessing and postprocessing are still best handled with TensorFlow.js. LiteRT.js only does inference; trying to do image resizing or normalization inside the inference graph adds unsupported operators. Using @litertjs/tfjs‑interop to pass pre‑processed tensors from TensorFlow.js to LiteRT.js keeps the workflow clean and avoids the tensor.dataSync penalty on WebGPU. By following these four steps—strict memory cleanup, backend testing with model‑teter, static‑shape PyTorch export, and TensorFlow.js interop for preprocessing—developers can unlock the promised privacy, low latency and zero server cost of LiteRT.js in real‑world web applications.
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