PrismML, a startup founded by Caltech researchers led by CEO Babak Hassibi, has released Bonsai 2 27B, a compressed version of Alibaba's open-source Qwen3.8 27B model that shrinks it from full size down to 5.9 GB. The compressed model retains about 98% of the original's benchmark performance, up from 95% in the prior Bonsai release, while being small enough to run on PCs and possibly high-end smartphones. The company has raised a $22.25 million seed round and counts Databricks co-founder Ion Stoica as an advisor.
techcrunch.com
· 2026-09-17
A new ternary-quantized model, Ternary Bonsai 2 27B, has been released, built on Qwen3.8 27B and using {-1,0,+1} weights with FP16 group scaling to shrink the model to about 1.76 effective bits per weight and a 5.9GB footprint. Despite being over 9x smaller than its full-precision counterpart, it retains 98.2% of aggregate benchmark performance across reasoning, coding, vision and agentic tasks, and supports a 262K-token context window under an Apache 2.0 license.
prismml.com
· 2026-09-17
A follow-up experiment tested four open models—DeepSeek V4 Flash, Inkling, Kimi K3, and Qwen3.8 A95B—by inserting the first 1% of GPT-5.5 Pro's reasoning trace into each model's own reasoning channel before letting it generate answers freely. Researchers then measured how much of GPT-5.5 Pro's visible answer text overlapped with each model's output. Qwen3.8 showed the largest jump, with overlap rising from 33.92% unprefilled to 54.50% with the GPT-5.5 Pro prefill, a 20.58 percentage-point increase, while other models showed much smaller shifts.
gist.github.com
· 2026-09-09
A benchmarking study tested several GGUF quantizations of the Qwen3.8 27B model against benchmarks including GPQA Diamond, IFBench, and Terminal-Bench 2.1. The 17GB Q4_K_M version performed on par with the full 55GB BF16 model on coding tasks and fits on a consumer 24GB GPU, while the smallest 6.2GB 1-bit version dropped to near-random accuracy on reasoning tasks, with longer reasoning chains making results worse.
quesma.com
· 2026-09-08
A new open-source tool called slotstream enables Apple Silicon Macs to run the 104GB Qwen3.8-Flash-Next language model even when the machine doesn't have enough RAM to hold it, by streaming weights from disk and dynamically sizing memory use, down to an 8.1GB floor. On a 48GB Mac it delivers about 12 tokens per second in warm decode with a roughly 3-second cold start, capping memory use at 32GB so the rest of the system stays usable. It requires macOS 14+, Apple Silicon, and about 110GB of free disk space for the one-time weight download.
github.com
· 2026-09-01
A user ran Alibaba's new Qwen3.8 27B model locally on a Mac Studio M3 Ultra for 10 days, using it for daily tasks like summarizing RSS feeds and renaming scanned PDFs, then formally benchmarked it against its predecessor. The Q4_K_M version generated at about 14 tokens per second versus 28.6 tokens/s for Qwen3.6 27B, but produced answers using roughly a third as many tokens, making total response time nearly equal. A 1-bit quantized version ran much faster at 27 tokens/s and stayed factually accurate but struggled to commit to definitive answers.
terminalbytes.com
· 2026-08-28
GoKawiil