BigMoeOnEdge/docs/limitations.md

Limitations and prior art

Prior art

BigMoeOnEdge is an engineering package, not a new technique. The ideas it combines:

• AirLLM — layer-by-layer streaming of >RAM models from disk.
• Apple, "LLM in a flash" — flash-aware weight streaming, windowing, sparsity-driven loading.
• FlexGen — offloading and I/O-bound throughput scheduling for large models.
• PowerInfer / EdgeMoE — hot/cold expert locality and expert-granularity residency on the edge.

The contribution here is a clean, modular, llama.cpp-native implementation of expert-selective streaming that stays lossless and runs on the public API — no fork for the serial path, and only a single ~25-line hook (with an explicit sunset) for the optional --overlap feature. See seam.md § 3.

Limitations

• One setting makes output non-reproducible. Every other knob is deterministic given a configuration: --n-expert-used changes the output, but changes it the same way on every run. --drop-cold-experts decides per routing from live cache state, so the same prompt and the same flags can decode differently run to run, and the byte-identity gates cannot cover its output — only its machinery. Off by default in the CLI.
• n=1 only. The expert sparsity exists only for single-token decode, so streaming is incompatible with speculative decoding or batching. Prefill streams the union of the prompt's routed experts (still far below the full bank, but larger than one token's).
• CPU experts. Streamed experts are computed on CPU; the rebind targets host memory. GPU offload of the streamed experts is not supported (the dense parts can still use the GPU). Decode is flash-I/O-bound anyway, so this is rarely the bottleneck.
• Shared experts stay resident. Architectures with an always-on shared expert (e.g. gemma4, deepseek4) stream the routed experts but keep the shared expert — and any dense layers — resident (in the page cache, or in the engine's own buffers under --dense-weights anon), so the streamed fraction (and the memory saving) is smaller than for a purely routed model like qwen3moe. The same applies to architectures whose first blocks are dense by design (lfm2moe has a leading_dense_block_count): those blocks name no expert tensors, so they are never streamed.
• A resident tensor can be larger than RAM, and then it is only ever mmap'd. qwen4exp (Qwen3.8-Flash-Next) carries a 51B n-gram embedding table (per_layer_token_embd, ~28.8 GB at IQ4_NL) that the graph reads sixteen rows at a time through get_rows. It is not indexed by expert, so the streamer does not bind it, and it is bigger than any phone's memory, so no dense policy can make it resident: such a tensor stays mmap'd whatever --dense-weights asks, and the engine says so at load. The streamed fraction of this architecture is therefore unusually low, and its per-token cost on that table is page faults on kilobyte reads rather than streamed expert bytes. That cost has not been measured on a device yet.
• Streaming does not help a model that fits. The engine's reason to exist is a model larger than RAM. Registering an architecture says the layout streams losslessly, not that streaming is the fast way to run every model using it — a small MoE that fits in memory is faster loaded resident, and the registry rows are about coverage, not a recommendation.
• Repack must stay off. Loading uses use_extra_bufts=false; you cannot combine streaming with weight repacking.
• Windows throughput. The cache's reserve-then-commit-per-slice path is heavier on Windows than the POSIX lazy-commit path. The gates run on Windows; the throughput targets are stated for Android/Linux.
• Depends on a ggml scheduling behaviour (documented in seam.md) that is not a stability-guaranteed contract. Re-verified by the gates on each submodule bump.

Not goals

• Distributing a model across devices (a different axis).
• Beating a model that already fits in RAM — if it fits, run it resident.