Meet Needle 2: An Open 45M-Parameter Tool-Calling Model That Ships as a 14MB Binary and Runs a Full Session in 28MB of RAM

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Cactus Compute has released Needle 2, an open 45M-parameter model for tool calling, device use, and structured extraction. The entire model ships as a single 14MB binary that runs a full session in about 28MB of RAM. Weights are trained and deployed at CQ2-bit using Cactus Quants, and the model is sealed inside the company’s own C++ engine, so there is no runtime to install and no download at inference time. Reported decode throughput is 500 tokens/sec on a Raspberry Pi 5, 400–1,500 tokens/sec on Meta Quest 3S and Apple Vision Pro, and 300–700 tokens/sec on sub-$200 phones. The design premise is narrow and stated plainly by the team: mapping a messy sentence onto a typed function signature needs no world knowledge and no open-ended prose. That framing is why 45M parameters are enough here, and why the model targets hardware with no GPU and no NPU.

Is it deployable?

Yes, Needle 2 ships as prebuilt binaries and a static library for macOS, Linux (x86-64, ARM64, ARMv7, RISC-V, MIPS32el), Windows, Android, iOS/watchOS/tvOS, and WebAssembly. Cactus says Pebble already runs Needle locally in the Index 01 app for offline voice actions.

Which companies: Any team shipping firmware or apps on constrained hardware. Seed-stage wearable and IoT startups, mid-market consumer-electronics OEMs, robotics teams, and large device makers needing an offline fallback. Cloud-first SaaS teams gain less.

Industries: smart home, wearables, low-end mobile, automotive in-cabin control, service robotics, retail kiosks and POS, routers and IP cameras, and regulated settings where audio cannot leave …

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