A 27B AI Model Shrinks to 5.9GB as Ternary Quantization Keeps Pushing the Floor Down

Bonsai 2 27B: A 27-Billion-Parameter Model in 5.9GB

On September 17, 2026, PrismML released Ternary Bonsai 2 27B, a compressed version of Alibaba's Qwen3.8 27B that stores nearly every weight as one of three values — minus one, zero, or plus one — plus FP16 group-wise scaling. The company's own release notes put the result at 1.76 effective bits per weight, a total footprint of 5.9GB down from roughly 54GB, and 98.2% retention of aggregate benchmark performance versus the full-precision original. It ships under Apache 2.0 and runs on Apple devices via MLX as well as NVIDIA GPUs via CUDA.

PrismML's own benchmark claims deserve the usual caveat — self-reported, aggregate scores can hide task-specific regressions. A hands-on writeup at DEV Community and independent testing threads on r/LocalLLaMA found the model coherent but not lossless, particularly on agentic coding tasks. Still, a 5.9GB file that behaves close to a 54GB one is the kind of compression that turns a workstation-only model into something a decent laptop or high-end phone can hold entirely in memory.

BITCOS Packs Ternary Weights Below the 1.585-Bit 'Floor'

Ternary large language models store weights as one of three values, and 1.585 bits per weight (log2 3) has long stood as the information-theoretic minimum for that scheme. Intel researchers, in a paper submitted to arXiv on September 14, 2026, argue that number was never the real floor: five-trit packing, the format actually used in practice, costs 1.625 bits per weight and silently assumes the three symbols occur equally often. Checking 29 real ternary checkpoints, the team found zeros making up as much as 51.5% of all weights — far from the roughly 33% equal probability would predict, as reported by Glonce.

Their new format, BITCOS, stores the location and sign of nonzero weights separately, exploiting that skew to reach as low as 1.485 bits per weight without retraining or altering the model's output. Per Intel's self-reported numbers relayed by WPS, decoding throughput improved by up to 18% on CPUs and 27% on GPUs, with separate unpacking kernels for AVX-512, AVX2, and Intel's Xe2 graphics. It's a storage trick, not a new model, but for edge devices where every megabyte of weight traffic costs latency and power, that trick is the whole ballgame.

SHADOW-50M Trades Chat Fluency for Exact Math and Disk-Based Memory

A newly released model called SHADOW-50M takes a different tack on small-model efficiency: instead of chasing chat quality, it targets reliable arithmetic and long-term memory. According to a writeup covering the project's GitHub repository and Hugging Face page, via TheNextGenTechInsider, the 44M-parameter model uses ternary {-1, 0, +1} weights for a 19.8MB deployment footprint, a frozen 73,880-token vocabulary represented as fixed fingerprints, and fixed computational circuits that trigger for tasks like date math and percentages rather than relying on tool calls.

The developer's self-reported figures claim roughly 1,900 tokens per second on a laptop CPU, about 41MB of RAM during operation, and successful retrieval from a 100M-token disk-based archive using just 28MB of resident memory — notably less than comparable vector-database setups. These are the builder's own benchmarks and haven't been independently reproduced, but the design goal is distinct from the usual small-LLM race: correctness on narrow, structured tasks rather than open-ended conversation, in a package small enough to fit almost anywhere.

Espressif Brings V4L2-Style Camera Capture to Arduino on ESP32

On September 20, 2026, Espressif's developer blog published a guide to using ESP_Video inside the Arduino core for ESP32, exposing a V4L2-style capture API that works across both MIPI-CSI and parallel DVP camera interfaces, according to the official post. The library gives Arduino sketches the same standardized frame-capture model Linux uses, rather than the ad hoc camera drivers ESP32 projects have historically relied on.

This is plumbing, not a headline feature, but plumbing is what determines whether a $5 camera module can feed a local vision model without a custom driver rewrite for every sensor. Standardizing capture on ESP32-S3 and newer chips with MIPI-CSI support lowers the bar for the next wave of on-device vision demos.

Laser Fault Injection Meets the RP2350

Raspberry Pi's own blog published findings from Ledger Donjon's security research into the RP2350 chip on September 18, 2026, detailing attempts to use laser fault injection to bypass the chip's secure boot and OTP protections, per the Raspberry Pi post. The write-up walks through the physical attack setup and what it revealed about the chip's hardware defenses.

Raspberry Pi Foundation publishing third-party attack research on its own silicon, rather than burying it, is unusual and useful: RP2350 boards increasingly run local inference and store credentials for connected pipelines, and knowing exactly where the physical security margins sit matters for anyone shipping the chip in a product rather than a hobby build.

Today's thread is the same one running under most quantization news this quarter: the theoretical floor for how small a model can get keeps moving, and every centimeter closer to that floor changes what fits on a phone, a laptop, or an $8 chip. Read the primary sources before trusting anyone's compression percentages, including PrismML's and Intel's own.