llama.cpp Splits Liquid AI's LFM3 Across Multiple NPUs While a 29MB Automation LLM Chases Microcontrollers

llama.cpp Learns to Split LFM3 Across Several NPUs

On November 1, 2026, the ggml-org/llama.cpp project merged pull request #31431, adding tensor-split support for Liquid AI's LFM3 family in build b12027. The PR's own benchmark tested LFM3-1.7B at Q4_0 on a Qualcomm SA8797P board, comparing one NPU against splits across two and four NPUs — both split configurations beat a single NPU on prompt processing and generation, though two NPUs edged out four on generation speed, according to AlCybr's write-up of the release.

The numbers come from one model size on one chip, so they say nothing yet about larger LFM3 variants or GPU setups. The commit history also lists 'Assisted-by: Claude Opus 5.5,' with the author disclosing AI assistance for testing — a small but telling detail about how llama.cpp contributors are now building their own benchmarks.

LFM3 uses a tri-hybrid architecture mixing gated convolutions and attention, and Liquid AI has been pushing it toward phones and dev boards. Multi-NPU tensor split matters because it removes the single-chip memory ceiling that kept hybrid models like this confined to whoever had the biggest individual accelerator.

A Sub-30MB Model Aims at Tool Calls, Not Chat

The GitHub repo cactus-compute/needle was trending on November 2, 2026, with the project describing itself as an 'automation foundation model for tiny devices' — 2-bit quantized, 8 to 29MB depending on variant, with claimed support for tool calls, ASR and structured extraction. It has climbed past 14,000 stars since its February 2026 creation.

The repo does not yet link an independent benchmark paper, and all size and capability claims are self-reported by the project. A model this small trades general chat ability for narrow, repeatable tasks, so its real test is whether it holds up on actual microcontroller-class hardware rather than a demo GPU.

If the claims hold, it fits a trend this beat keeps tracking: purpose-built sub-30MB models for specific automation jobs, rather than squeezing a general chatbot onto hardware it was never meant for.

A Patch Set Claims 3.3x Faster MoE Offload on One Desktop GPU

On November 1, 2026, a Reddit post in r/LocalLLaMA introduced tandem.cpp, a patch set for llama.cpp that keeps a mixture-of-experts model's most-used experts on GPU VRAM while the CPU handles the rest. The author reports Qwen3.8-Flash-Next running at 97 tokens/second on a single RTX 4090 plus 64GB of system RAM, a claimed 3.3x speedup over mainline llama.cpp, and says the approach works for any MoE model.

These are self-reported, single-machine numbers with no independent reproduction yet, and 'works for any MoE' is the author's own framing, not a tested claim across model families.

The underlying problem — a MoE model too big for one GPU's memory — is the same constraint edge NPUs face at far smaller scale, which is why offload tricks like this one are worth watching even on a desktop rig.

Raspberry Pi Raises 16GB Prices Again

Raspberry Pi's official blog confirmed on November 2, 2026 that prices are rising for some 16GB products, citing increased component costs. Hackster's coverage puts the new Raspberry Pi 5 16GB price at $385, with the 16GB Compute Module 5 also going up — the second such increase in recent months, per Hackster's writeup.

The Raspberry Pi 500+ keeps its price, according to the official post, so this hits the SBC line specifically, not the full lineup.

For anyone running local LLMs on a Pi 5 — a common budget choice on this beat — the memory-heavy 16GB SKU just got noticeably more expensive, pushing more makers toward 8GB boards or alternative SBCs.

Banana Pi Pitches an Allwinner A733 Board as a Pi 5 Alternative

CNX Software reported on November 2, 2026 that Banana Pi's BPI-M8 pairs an Allwinner A733 octa-core Cortex-A76/A55 SoC with a PCIe FFC connector and a UFS module socket on a credit-card-sized board explicitly aimed at Raspberry Pi 5 users.

The article does not list a price or ship date yet, so this is a spec reveal rather than a confirmed launch — worth tracking, not buying, until Banana Pi publishes availability.

Timing matters here: as Raspberry Pi's own 16GB prices climb, every credible non-Pi SBC with PCIe and fast storage becomes a more serious option for running local inference stacks.

None of today's numbers are peer-reviewed, and most are self-reported — treat the multi-NPU tensor splits, the 3.3x MoE claim, and the sub-30MB model's benchmarks as starting points, not settled facts, until someone outside the original repo reproduces them.