Recently, I'm trying to deploy some third-party LLM to Apple devices. The methodoloy is similar to https://github.com/Anemll/Anemll. The biggest issue I'm having now is the runtime memory usage. When there are multiple functions in a model (mlpackage or mlmodelc), the runtime memory usage for weights is somehow duplicated when I load all of them. Here's the detail: I created my multifunction mlpackage following https://apple.github.io/coremltools/docs-guides/source/multifunction-models.html I loaded each of the functions using the generated swift class: let config = MLModelConfiguration() config.computeUnits = MLComputeUnits.cpuAndNeuralEngine config.functionName = "infer_512"; let ffn1_infer_512 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) config.functionName = "infer_1024"; let ffn1_infer_1024 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) config.functionName = "infer_2048"; let ffn1_infer_2048 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) I observed that RAM usage increases linearly as I load each of the functions. Using instruments, I see that there are multiple HWX files generated and loaded, each of which contains all the weight data. My understanding of what's happening here: The CoreML framework did some MIL->MIL preprocessing before further compilation, which includes separating CPU workload from ANE workload. The ANE part of each function is moved into a separate MIL file then compile separately into a HWX file each. The problem is that the weight data of these HWX files are duplicated. Since that the weight data of LLMs is huge, it will cause out-of-memory issue on mobile devices. The improvement I'm hoping from Apple: I hope we can try to merge the processed MIL files back into one before calling ANECCompile(), so that the weights can be merged. I don't have control over that in user space and I'm not sure if that is feasible. So I'm asking for help here. Thanks.