Bonsai 2 (Qwen 3.8 27B) as an OpenClaw Fallback: 8GB, 85 tok/s on a 5070

PrismML's Ternary-Bonsai-2-27B is a quantized Qwen3 3.8 27B build that a r/openclaw user is running locally as an OpenClaw fallback model when their ChatGPT and Grok quotas run dry. Their claim: ~8GB footprint, 98% of the base Qwen3 27B's quality retained, and text plus vision support.

Numbers from the setup

• Output speed: 85 tok/s
• Hardware: RTX 5070 with 16GB VRAM, 64GB system memory
• Disk footprint: ~8GB
• Quality retention: reported 98% of Qwen3 3.8 27B

The two GGUFs you need

Files come from the prism-ml/Ternary-Bonsai-2-27B-gguf repo on Hugging Face:

Ternary-Bonsai-2-27B-PQ2_0.gguf        (text)
Ternary-Bonsai-2-27B-mmproj-Q8_0.gguf  (vision)

Both are required if you want the multimodal path — the mmproj file is the vision projector, the PQ2_0 file is the ternarized text weights.

Setup notes

Bonsai 2 requires PrismML's own fork of llama.cpp — it will not load in stock llama.cpp. The author had GPT wire up the fork on a separate AI machine from the OpenClaw host, with the model configured to:

• Dynamically load when OpenClaw calls it
• Unload after 10 minutes of inactivity
• Act as the fallback when GPT 5.6 and Grok hit usage limits

What it actually did

The interesting part isn't the tok/s — it's the agentic behavior. Per the post, it handled web search via a browser, VM control, installing and using new software, and running existing workflows, to the point the author says they "can't tell it's not a frontier model."

That's a single user's report, not a benchmark, so treat the quality claims as anecdotal. The load/unload-on-idle pattern is the practical takeaway: it keeps a local 27B resident only when your paid provider is rate-limited, which is a sensible way to avoid permanently burning VRAM on a box that's also doing other work.

If you have a GPU with 16GB+ VRAM, the PQ2_0 + mmproj pair is small enough to be worth testing against your own agentic tasks before deciding whether it holds up for you.