Text / prism-ml

Bonsai 27B · original 1-bit

The original Qwen3.6-based Bonsai 27B in its smaller 1-bit GGUF package. Bonsai 2 is a separate, newer generation.

EstimatedRepository opened Jul 4, 2026Source checked 9/23/2026Version: f10afb35
LOCALRENTED GPUOPEN WEIGHTS
Freshness 67 out of 100. Repository age: 103 days old, Jul 4, 2026. Measures age, not quality.
Popularity 76 out of 100. 403,985 downloads in 30 days; 920 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source
Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source
Scores as of Oct 15, 2026. Statistics checked Oct 15, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

At a glance

Architecture
unknown
License
apache-2.0
Disk space
3.5 GB
Software
PrismML llama.cpp fork
View the model source
MY HARDWARE

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Which Bonsai is this?

Bonsai 2 is the newer Qwen3.8-based release. The original Bonsai 27B is Qwen3.6-based and has separate ternary and smaller 1-bit packages. A different fit badge can also reflect different evidence: Bonsai 2 has a publisher RTX 5090 measurement; the original packages have a memory-based estimate for that card.

Best for

  • Text generation via llama.cpp using GGUF Q1_0_g128 weights
  • Conversational or agentic tasks on local hardware where the model card describes support
  • On-device inference with CUDA, Metal, or CPU backends as listed in the model card
  • Long-context work up to the stated 262K-token window with the described 4-bit KV cache
  • Speculative decoding on a CUDA serving path using the provided DSpark drafter

Tradeoffs

  • Facts not stated in the source data, including specific compatibility beyond the listed backends, are not inferred
  • The model card does not state performance guarantees; any benchmark or speed figures are source claims and are not independently verified here
  • Hardware requirements are not fully specified; the source only mentions CUDA, Metal, CPU, and some example devices
  • Quantized 1-bit weights and the custom kernels may require the linked llama.cpp fork; support outside that setup is not established in the source
  • No purchase recommendation, testing claim, or license interpretation beyond the provided apache-2.0 metadata is made

Ways to run it

PrismML llama.cpp fork · Windows, macOS, Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

./build/bin/llama-cli -m Bonsai-27B-Q1_0.gguf -p "Explain quantum computing in simple terms." -n 256 --temp 0.7 --top-p 0.95 --top-k 20 -ngl 99
Official instructions

Explore its uses

GGUFTEXT-GENERATION

Keep exploring

Bonsai 27B · original 1-bit: hardware, VRAM & setup | YouRunAI