Show HN: Run an 80B Qwen in 4.3 GB of RAM on a Mac, and a 35B on an iPhone

Swiftlet, a Swift + Metal runtime for Qwen3-Next and Qwen3.5/3.6 MoE hybrid models, runs an 80B Qwen in 4.3 GB of RAM on a Mac and a 35B on an iPhone 17 in about 2.5 GB of RAM at about 1 token per second, marking the first time a model of this class has run natively on a phone. The runtime keeps only the dense core resident and streams routed Mixture-of-Experts weights from storage, with both models generating validated output. The project is open source on GitHub, and the 35B model is available in the Priv AI app on the App Store.

Run 35B and 80B Qwen models on ordinary Apple devices, including iPhones. Swiftlet is a Swift + Metal runtime for the Qwen3-Next and Qwen3.5/3.6 MoE hybrid model family. It keeps only the small dense core of a model resident in memory and streams the routed Mixture-of-Experts weights from storage on demand. The result: | Model | Disk | Peak RAM | Decode speed M5 Mac | |---|---|---|---| | Qwen3-Next-80B-A3B, 4-bit https://huggingface.co/Leonickson/Qwen3-Next-80B-A3B-qpack The 35B also runs on an iPhone 17 in about 2.5 GB of RAM, at about 1 tok/s today. As far as we know, that is the first time a model of this class has run natively on a phone. Status: working end to end. Both models generate correct, validated output. The current focus is kernel speed the decode loop is dispatch bound, not IO bound, so there is clear headroom . One expectation to set honestly: only about 3B parameters are active per token, so these models chat and write like large models but recall facts like small ones. git clone https://github.com/leonickson1/Swiftlet.git && cd Swiftlet swift build -c release Download the 35B container from Hugging Face resumable : .build/release/swiftlet-repack \ --from-hf Leonickson/Qwen3.6-35B-A3B-qpack \ --output ~/models/qwen3.6-35b.qpack Or the 80B 42 GB on disk, still only ~4.3 GB of RAM : .build/release/swiftlet-repack \ --from-hf Leonickson/Qwen3-Next-80B-A3B-qpack \ --output ~/models/qwen3-next-80b.qpack Chat applies the model chat template, disables the reasoning block, keeps conversation state so follow-ups prefill only the new turn : .build/release/swiftlet chat ~/models/qwen3.6-35b.qpack \ "Who wrote One Hundred Years of Solitude?" "What language did he write it in?" One-shot generation with stats: .build/release/swiftlet generate ~/models/qwen3.6-35b.qpack \ --gpu --chat --prompt "Explain expert streaming in one paragraph." OpenAI-compatible server loopback only : .build/release/swiftlet-server --model ~/models/qwen3.6-35b.qpack --port 8080 The same command also repacks raw MLX checkpoints --from-hf mlx-community/... or --source /path/to/checkpoint . Requirements: Apple Silicon, macOS 14+ or iOS 17+, free SSD space for the container 18 GB for the 35B, 42 GB for the 80B . The 35B runs on iPhone inside Priv AI on the App Store : open Settings, then Experimental Models, and download the model. It streams from storage and chats on-device with no server involved. The Experimental Models feature ships in the newest app version, which is still in App Store review, so it may not appear for a couple of days. If you want the phone experience today, build the app from source: the app is open source at leonickson1/localLLM https://github.com/leonickson1/localLLM . Clone this repo next to it as swiftlet , open the Xcode project, and run it on your iPhone. These models activate only about 3B of their parameters per token. Each layer routes every token to 10 of 512 experts 80B or 8 of 256 35B . Swiftlet: - keeps the dense weights resident: attention, DeltaNet projections, routers, shared experts, embeddings. About 1.3 GB 35B or 2.5 GB 80B at 4-bit; - repacks the tens of thousands of routed experts into fixed-stride blobs in a .qpack container, so fetching one expert is exactly one pread from SSD, no mmap and no page-cache thrash; - caches hot experts in a bounded pool with LFU plus recency eviction. Cache size barely affects speed measured 43 to 70 percent hit rates at the same throughput , because Apple SSDs absorb the misses; - runs the whole forward pass on Metal with runtime-compiled shaders, so no Metal toolchain is needed at build time and the same code ships on iOS. 75 percent of the layers use Gated DeltaNet linear attention with a fixed-size recurrent state, so there is no growing KV cache for those layers at any context length. Swiftlet is a library first: The Swift package. Add SwiftletCore to any macOS or iOS app and use SwiftletSession for chat with streaming deltas, conversation caching, sampling with repetition control, and memory-pressure handling built in. The CLI. swiftlet chat and swiftlet generate for local use and benchmarking, swiftlet-repack to build containers from MLX checkpoints including streaming straight from Hugging Face with resume . The server. swiftlet-server speaks the OpenAI chat-completions API on loopback, so any chat UI that talks to OpenAI-compatible endpoints can use a streamed local model. An app. Priv AI https://apps.apple.com/us/app/priv-ai/id6765706001 on iOS embeds SwiftletCore as its streamed-model engine. End users tap Download and chat. Nothing here is terminal-only. The app itself is open source at leonickson1/localLLM https://github.com/leonickson1/localLLM if you want to build it yourself clone this repo next to it as swiftlet . Every layer of the forward pass Gated DeltaNet recurrence, gated GQA attention, sparse MoE routing is validated against mlx-lm reference implementations with per-layer fixtures, in f32 and int4 quantized form. Incremental decoding is verified against whole-sequence processing. Metal kernels are tested against the exact CPU reference, and the fast and scalar GPU kernels are verified to produce identical outputs. Containers are byte-verifiable against their source checkpoints. Streaming placement never changes model semantics: an expert answers identically from cache or disk. swift test TurboFieldfare https://github.com/drumih/turbo-fieldfare proved the expert-streaming thesis for Gemma on Macs, and Swiftlet adopts several of its published design lessons with gratitude: stream experts with pread into a bounded slot pool instead of mmap, evict with LFU plus recency, pack experts at fixed stride so one fetch is one read, install by routing downloaded bytes straight into their final container positions, and compile shaders at runtime. Everything else is built here, from scratch, in about 10k lines of Swift and Metal written against mlx-lm references rather than TurboFieldfare code: - support for a different model family with a fundamentally different architecture: the Qwen hybrid stack with Gated DeltaNet linear attention, gated GQA, and high-sparsity MoE with a shared expert TurboFieldfare runs Gemma, a classical dense transformer ; - MLX affine int4/int8 group quantization compute in Metal, byte-addressed kernels with 64-bit offsets for multi-gigabyte shards, a cooperative simdgroup GEMV fast path, and explicit hazard management; - a validated CPU reference implementation and the fixture infrastructure that gates every kernel change; - the .qpack container and repacker, the resumable Hugging Face streaming installer with stall recovery, and download cancellation; - the chat session layer: template handling for thinking and non-thinking Qwen variants, sampling with presence and frequency penalties and minimum-length and sentence-completion stopping, conversation caching with delta prefill, and iOS memory-pressure coordination; - iPhone support end to end, including the app engine integration. colibrì https://github.com/JustVugg/colibri informed the caching and placement policy thinking. mlx-lm is the correctness reference throughout. Swiftlet was built in collaboration with Claude Code https://claude.com/claude-code . Apache 2.0. Model weights are downloaded separately and remain governed by their own terms Qwen models: Apache 2.0 . See THIRD PARTY NOTICES.md.