Platform and backend support matrix

Use this page to check whether a runtime or feature works on a platform. LlamaBackend() picks the runtime from the model format: LiteRT-LM bundles run on LiteRT-LM, and GGUF files run on llama.cpp natively or on the WebGPU bridge in a browser. Native targets read the file header; web targets use the URL's extension or ModelSource.format (How routing works). To choose backend modules or leave a runtime out of the app, see Native runtime configuration.

At a glance#

Desktop source-CLI diagnostics can opt into dart run llamadart:doctor_native --runtime llama_cpp --json (or litert_lm / stable_diffusion) to check a declared artifact in an isolated process. It checks selected symbols/static ABI markers, without device or model initialization. This adds no runtime/platform/model capability: production loader behavior, loaded release identity, emitted app assets and GPU/inference remain unchecked. See scope and limits.

On Windows, the llama.cpp and LiteRT-LM bundles ship the Visual C++ runtime DLLs they need, so they need no Visual C++ Redistributable. The opt-in stable_diffusion runtime does not ship them: machines that run it need the latest Microsoft Visual C++ v14 Redistributable (x64), which stock Windows Server lacks, and its load error names the DLLs that failed to load.

With the pinned bridge assets, Web speech-to-text and text-to-speech cancellation is request-scoped, including queued STT generation and TTS startup awaiting a capability probe.

Speech to text, text to speech and decision models are experimental. Image generation is a Preview: its API is experimental and may change, and it needs the opt-in stable_diffusion runtime. Per-platform requirements, validated models, memory figures and the bounded recipes for tiling, editing, ControlNet, hires fix, reference editing, seamless textures, module placement, the conditioning cache, FP8 weights and local checkpoint sets are in Image generation.

GGUF Qwen3-ASR accepts WAV, MP3 and FLAC; real-model checks cover all three on native macOS and in headless Chromium. LiteRT-LM ASR is CPU-only streaming recognition. Decision models run on llama.cpp and WebGPU only; on LiteRT-LM DecisionEngine.load and attach throw LlamaUnsupportedException. See Speech to text, Text to speech and Decision models.

Compute device#

ModelParams.device (ComputeDevice) applies to every runtime. auto keeps each runtime's default; cpu, gpu and npu run there or throw LlamaUnsupportedException, never on another device. See Choosing the device.

The WebGPU bridge ignores negative mainGpu values, including with splitMode: ModelSplitMode.none; they do not select the CPU as they do in native llama.cpp. Explicit gpu still requires WebGPU after load.

Under auto, gpuLayers: 0 or a CPU or BLAS preferredBackend selects the CPU on both llama.cpp and LiteRT-LM, and a GPU preferredBackend (Vulkan, Metal, CUDA, OpenCL or HIP) selects the LiteRT-LM GPU backend. On Linux x64 and Windows x64, the LiteRT-LM GPU backend is not CUDA; use device: cpu on hosts without a hardware Vulkan driver. Windows arm64 has no LiteRT-LM runtime. The deprecated liteRtLmBackend selector works until 1.0; an unavailable choice there throws LlamaModelException.

LlamaBackend() rejects a LiteRT-LM model with LlamaUnsupportedException before any download on macOS x86_64, Windows arm64 and the x86_64 iOS simulator, which have no LiteRT-LM runtime, and for an explicit device the platform's LiteRT-LM lacks, such as gpu on Linux arm64. The message names the ABI. This applies when ModelSource.format or the file name says .litertlm; a model known only from its file header is rejected when it loads.

await engine.checkLoad(model, params: params) returns the same verdict as a LlamaLoadCheckReport without downloading, reading a file or starting a runtime: unsupported with the exception the load would throw, deferred when only the file header can tell the format, unknown when the backend cannot tell, as with LlamaEngine(LiteRtLmBackend()), and otherwise supported, with the format, runtime and device the load would use. Like the setup report, it is policy: native libraries, browser assets, auto device selection, LoRA limits, download options and the file are checked by the load (backend selection).

Dart-side chat-template rendering accepts Jinja \xHH string escapes on native and web. Model architecture support depends on the pinned runtime; see chat templates.

Features by runtime#

Tool-result media are LlamaToolResultContent.content parts; LlamaEngineCapabilities.toolResultMedia reports them. Other formats, audio, video and URL-only images throw LlamaUnsupportedException, as do tool-role image/audio parts and media alongside LlamaToolResultContent under any role (tool-result media).

LlamaEngine.supportsVideo returns false on every runtime, and passing LlamaVideoContent throws LlamaUnsupportedException. Web state paths point into the bridge's WASMFS virtual filesystem; to keep state across reloads, export and import it in app code. The pinned bridge assets report the WebGPU LoRA, thinking-budget, presence-penalty, Min-P and speculative decoding capabilities; older assets report none of them (Older bridge assets). After a load, LlamaEngine.runtime names the runtime and LlamaEngine.capabilities reports the rows of this table for the loaded model: media input, embeddings, next-token scores, multi-turn chat, tools, structured output and grammars, sampling controls and speculative decoding. Guides: LoRA adapters, Tool calling, Performance tuning, Multimodal, Embeddings, Observing operations.

Known limitations#

Device, OS and driver limits on iOS, macOS, Android, Linux, Windows and the web are on Known platform issues.

Pinned runtimes#

The native-assets hook currently pins llamadart-native tag v0.8.0-1 and litert-lm-native release v0.18.1-4 (lib/src/hook/native_release_pins.dart). Apps can override the llama.cpp release with llamadart_native_tag; accepted tag forms, keys, backend modules and local bundles: Native runtime configuration.

Apps load the bridge assets themselves; see Add the bridge to your app. Linux system libraries: Linux prerequisites.