A new implementation demonstrates WebAssembly linear memory can be directly shared with Metal GPU buffers on Apple Silicon via unified memory, enabling zero-copy matrix operations for AI inference without serialization or intermediate buffers.

Primary source details three verified links: mmap with 16 KB alignment on ARM64 macOS, MTLDevice.makeBuffer(bytesNoCopy:) preserving pointer identity with 0.03 MB RSS delta, and Wasmtime MemoryCreator trait returning the same backing pointer (https://abacusnoir.com/2026/04/18/zero-copy-gpu-inference-from-webassembly-on-apple-silicon/). Original coverage omits how this mirrors techniques in Apple's MLX framework released 2023 which exploits unified memory for tensor compute without copies across M-series chips (https://github.com/ml-explore/mlx). It also understates prior impedance mismatches documented in WASI-NN proposals that required explicit host-device transfers.

Related WebGPU specification work at W3C similarly targets low-overhead accelerator access from sandboxed environments but has not yet achieved zero-copy Wasm linear memory sharing on unified architectures (https://www.w3.org/TR/webgpu/). Patterns from discrete GPU paths such as CUDA pinned memory show two-copy penalties of 20-40% latency; Apple Silicon erases the PCIe bus entirely per 2020 M1 architecture whitepaper. Coverage missed portability limits: technique is Apple-specific and does not extend to non-unified systems without reintroducing copies.

Synthesis indicates this control-plane/compute-plane split could reduce inference latency in browser ML runtimes such as WebLLM while keeping all data in-device, aligning with on-device AI shifts reported in Core ML updates since 2022. Measurements confirm pointer equivalence and identical compute times versus explicit paths.