Bonsai 27B runs at 44 tok/s (44.2 as recorded) on an M5 Pro, a published figure reported by huggingface.co (llama.cpp, Q1_0_g128, tg128). It needs about 6 GB of unified memory. It is Prism ML's 27B dense model, released under the Apache 2.0 licence.
Want to run Bonsai 27B faster — or step up to bigger models? Find your Mac →This is a published figure: a real run, reported by its source and linked below. The index records it as given and never adjusts it.
| Metric | Value |
|---|---|
| Tokens per second | sourced44 tok/s |
| Provenance | published figure · huggingface.co |
| Quantization | Q1_0_g128 |
| Engine | llama.cpp |
| Context | tg128 |
| Figure dated | 2026-07 |
| Minimum RAM | 6 GB |
| Parameters | 27B |
| Chip bandwidth | 307 GB/s |
Each row as the index holds it, with its source. “Mac RAM” is the memory of the Mac a run used, not the model’s requirement (6 GB). The headline figure follows the reference rule: the lower-median measured plain-decode run at 5 bits or fewer (a run whose quant was not stated counts only when no stated-quant run qualifies), else the estimate; a run marked “not used to anchor estimates” is listed here but never becomes the headline.
| Speed | Quant | Engine | Mac RAM | Context | Source | Note |
|---|---|---|---|---|---|---|
| sourced44.2 tok/s | Q1_0_g128 | llama.cpp | — | tg128 | huggingface.co | Vendor model card: PrismML llama.cpp fork (Metal), llama-bench; 3.9 GB. |
Ollama — the index has not verified an Ollama tag for Bonsai 27B, so it doesn’t print one (a guessed tag fails with “pull model manifest: file does not exist”). Search the Ollama library →
MLX — pick a build from Hugging Face’s MLX listings, then run it with its repo id:
Downloads: Search the Ollama library → · model card →
The 44 tok/s figure above came from llama.cpp at Q1_0_g128; other runtimes and quants run at different speeds.
Prefer a GUI? LM Studio downloads GGUF and MLX builds in one click. Step by step: install Ollama on a Mac.
Prism ML's 1-bit 27B (Q1_0_g128, 3.9 GB), Apache 2.0, so it runs on an 8 GB Mac. 1-bit decoding is kernel-bound, so the index quotes only vendor figures: 66.4 tok/s on an M5 Max, 44.2 on an M5 Pro and 26.0 on an M4 Pro (PrismML llama.cpp fork, tg128). Its ternary successor is Ternary Bonsai 2 27B.
Apache 2.0 licence · 27B · 262K context · LLMCheck capability 33/50 · see its leaderboard rank →
Bonsai 27B needs about 6 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:
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See how Bonsai 27B stacks up against other models on your specific Mac hardware.
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