Swiftlet Packs 80B Qwen Into Mac With 4.3GB Memory; iPhone 17 to Run 35B Natively

A Swift + Metal runtime named Swiftlet is redefining "where large models can run." It is specifically tailored for the Qwen3-Next and Qwen3.5/3.6 family of MoE (Mixture of Experts) models. The core idea is quite clever: only the small dense core of the model stays in memory, while the large part—the routing expert weights—normally resides on SSD and is streamed in as needed.

The results are clear from the numbers. On an M5 Mac, the 4-bit version of Qwen3.6-35B-A3B takes up 18GB on disk but only peaks at 2.6GB of memory, decoding at 7 to 11 tok/s; even more impressive is the 4-bit version of Qwen3-Next-80B-A3B, which requires 42GB on disk but peaks at just 4.3GB of memory, with a speed of 4.5 to 5 tok/s. The 35B version can now run on an iPhone 17, with about 2.5GB of memory and a speed of around 1 tok/s — according to the developers, this is the first time such a model runs natively on a phone without touching a server.

The project is fully end-to-end usable, and both models can produce verified correct outputs. The developer also admits a trade-off: each token actually activates about 3B parameters, so these models behave like large models during conversations and writing, but still act like small models in terms of factual memory.

Its working principle lies in the fine-grained scheduling. Each layer routes each token to 10 out of 512 experts (for the 80B version) or 8 out of 256 experts (for the 35B version). Swiftlet keeps the dense weights — attention, DeltaNet projections, routers, shared experts, and embedding vectors — firmly in memory, taking about 1.3GB (for 35B) or 2.5GB (for 80B) in 4-bit. Thousands of routing experts are repackaged into fixed-step data blocks, stored in .qpack containers. To fetch one expert, it only needs one pread operation on the SSD, no mmap, and it doesn't disturb the page cache. Popular experts are cached in a limited pool using LFU plus recentness strategy for eviction; the hit rate ranges from 43% to 70%, and the cache size has almost no impact on speed, as Apple's SSD can handle the overhead of misses.

The entire forward pass runs on Metal using runtime-compiled shaders, so there's no need for a Metal toolchain during build, and the same code can be directly deployed to iOS. More interestingly, 75% of the layers use Gated DeltaNet linear attention with a fixed-size cyclic state, meaning that regardless of the context length, it never grows an expanding KV cache — the hidden burden of long text conversations is quietly offloaded.

Swiftlet isn't just a command-line toy. It has four identities designed for itself. As a library, SwiftletCore can be embedded into any macOS or iOS app, providing chat capabilities with streaming incremental output and conversation caching; as a command-line tool, swiftlet chat and swiftlet generate handle local execution and benchmarking, while swiftlet-repack builds containers directly from MLX checkpoints and supports resuming downloads from Hugging Face. As a server, swiftlet-server provides OpenAI-compatible chat-completions interfaces on a loopback address, allowing any OpenAI-compatible chat interface to connect to the local model; as an application, the iOS version of Priv AI embeds SwiftletCore as a streaming model engine, allowing regular users to start chatting by simply downloading it.

Regarding correctness, every layer's forward pass is compared layer-by-layer against the mlx-lm reference implementation, covering f32 and int4 quantization forms. Incremental decoding is also compared with full sequence results, and the Metal kernels have been repeatedly verified against precise CPU references. The container can also perform byte-level verification against the source checkpoint — whether experts are read from cache or disk, the answers are exactly the same.

Its inspiration path is clearly explained: TurboFieldfare first validated the feasibility of streaming experts for Gemma on Mac, and Swiftlet borrowed some publicly available design experiences from it, such as using pread to stream experts into bounded slot pools, using LFU plus recentness for eviction, and using fixed-step packaging so that one read equals one fetch. However, the rest was written from scratch, with about 10,000 lines of Swift and Metal code, tackling the completely different Qwen mixed architecture: Gated DeltaNet linear attention, gated GQA, and high-sparse MoE with shared experts. It even implements MLX-style int4/int8 group quantization calculations in Metal, using byte-addressable kernels and 64-bit offsets to support gigabytes of shards.