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# Bonsai 2 27B Full 27B-class reasoning in ternary transformer weights — on everyday laptops > **8.60 GB** on disk, language model + vision tower | **98.2%** of FP16 intelligence retained | **\~47 tok/s** on an Apple M5 Max laptop ## Highlights - **8.60 GB** on disk: a 7.67 GB language model (down from \~54 GB FP16) plus the 0.92 GB vision tower. MLX's container stores a scale and a bias per group, so the language model costs 2.25 bits/weight where the same ternary weights take 1.75 in the GGUF PTQ1_0 packing - **98.2% of FP16 intelligence retained**: 84.78 average across 14 thinking-mode benchmarks — far above the conventional IQ2_XXS build (72.59) at less than two-thirds of its footprint, and within 0.4 points of UD-Q4_K_XL at three times the footprint - **Retains thinking, reasoning, and agentic behavior** deep in the sub-4-bit regime, where conventional low-bit representations collapse: math within half a point of full precision (96.57), coding level with the baseline (89.42), agentic tool calling at 74.92 - **End-to-end ternary language weights** across embeddings, attention projections, MLP projections, and LM head, with no high-precision escape hatches behind a low-bit label — a *true* 1.72 bits per weight as a representation, 2.25 as MLX stores it; the vision tower is bundled in this pack, unquantized - **262K-token context** on-device, kept practical by the Qwen3.8-27B hybrid-attention backbone (\~75% linear attention) - **Custom ternary hybrid-attention kernels** on Apple MLX (Python, Swift) and CUDA — packed weights are consumed directly, never expanded back to FP16 - **GGUF companion**: also available as [Ternary-Bonsai-2-27B-gguf](https://huggingface.co/prism-ml/Ternary-Bonsai-2-27B-gguf) for llama.cpp (CUDA, Metal, CPU), in two packings — PTQ1_0 (5.95 GB) and PQ2_0 (7.21 GB) ## Resources - **[Whitepaper](https://github.com/PrismML-Eng/Bonsai-demo/blob/main/bonsai-2-27b-whitepaper.pdf)** — full methodology, benchmarks, and measurement notes - **[Demo & examples](https://github.com/PrismML-Eng/Bonsai-demo)** — **the source of truth for running these models**: tested setup for every backend, pinned binaries, serving, benchmarking and integration, kept current as the runtimes move - **Low-bit kernels**: [MLX fork](https://github.com/PrismML-Eng/mlx) (Apple Silicon) · [mlx-swift fork](https://github.com/PrismML-Eng/mlx-swift) (iOS/macOS) · [llama.cpp fork](https://github.com/PrismML-Eng/llama.cpp) (CUDA) - **[Discord](https://discord.gg/prismml)** — join the community for support, discussion, and updates ## Model Overview | Item | Specification | | :---------------- | :----------------------------------------------------------------------------------------------- | | Base model | Derived from Qwen3.8-27B, a 27B hybrid-attention causal language model (architecture unchanged) | | Parameters | 27.36B total — 24.35B language backbone (64 blocks) + 2.54B embedding/LM head + 0.46B vision tower (27 blocks) | | Architecture | Hybrid attention (\~75% linear / \~25% full attention), SwiGLU MLP, RoPE, RMSNorm | | Context length | 262K tokens (inherited from the base model; kept practical on-device by the predominantly linear-attention backbone) | | Weight format | Ternary g128: {−1, 0, +1} weights with FP16 group-wise scaling | | Weight basis | Blockwise Hadamard rotation (block 1024, fixed ±1 signs) folded into the stored weights; the matching transform is applied to activations at runtime | | Low-bit coverage | Embeddings, attention projections, MLP projections, LM head | | Vision tower | included in this pack, 0.92 GB FP16, the official Qwen3.8-27B tower, unquantized | | Deployed size | **8.60 GB** MLX safetensors on disk: 7.67 GB language model + 0.92 GB vision tower | | Backends | Apple MLX (Python, Swift) and CUDA | | License | Apache 2.0 | ## Weight Representation: Ternary g128 Each weight takes a value from {−1, 0, +1}, with one shared FP16 scale factor for every group of 128 weights. A ternary value carries log₂3 ≈ 1.585 bits of information, so the effective storage cost of the format is **\~1.71 bits/weight** (ternary code + 16-bit scale amortized over 128 weights); counting the small set of tensors held above the ternary representation brings the model as a whole to **1.72 bits/weight** — an idealized \~9.3x reduction vs FP16. The weights are stored in a **rotated basis**: each matrix is transformed blockwise by an orthogonal Hadamard rotation before the ternary assignment, and the runtime applies the matching transform to activations. The rotation is folded into the stored weights offline, so it costs no extra bits and no extra weight traffic; the packed model declares its rotation as metadata, so a runtime either applies the matching transform or refuses to load the file. ### Memory Requirement | Format | True bits/weight | Size | Reduction | | :---------------------------- | ---------------: | ----------: | ----------: | | FP16 (baseline) | 16.0 | \~54 GB | 1.0x | | **Ternary g128 (ideal)** | **1.72** | **5.8 GB** | **\~9.3x** | | PTQ1_0 (dense trits, GGUF) | 1.75 | 5.95 GB | \~9.0x | | PQ2_0 (2-bit slots, GGUF) | 2.13 | 7.21 GB | \~7.5x | | **MLX 2-bit (this repo)** | **2.25** | **7.67 GB** | **\~7.0x** | Those are language-model figures; the pack on disk is 8.60 GB once the 0.92 GB vision tower is included. Practical deployment needs packing formats that efficient kernels can consume, and Bonsai 2 ships two: **PTQ1_0** packs trits densely and lands essentially on the information-theoretic target, while **PQ2_0** stores each trit in a 2-bit slot, trading footprint for cheaper unpacking. Neither is uniformly faster — see the throughput table below. These sizes describe the language model alone, the only component that must stay resident for text inference; 26.2M parameters (**0.0976%** of the language model — the recurrent state path of the linear-attention layers, plus the normalization weights) remain in higher precision and are counted in the 1.72 figure. Unlike conventional low-bit builds — whose advertised labels understate their true average bit-width (a widely-used "2-bit" build of Qwen3.8-27B is really 2.8 bits/weight at 9.4 GB) — the Bonsai representation carries a bit-width that matches its name. ### MLX Packaging This pack carries **both** the language model and the vision tower. `model.safetensors` is 8.60 GB: 7.67 GB of packed language weights and 0.92 GB for the tower, which is the official Qwen3.8-27B vision tower carried unrotated and unquantized in FP16. The Hadamard rotation applies only to the language model's projections, so the tower needs no transform and is plain passthrough. MLX's grouped low-bit format stores both a scale and a bias per group. The ternary levels `{-s, 0, +s}` are reproduced exactly by setting `scale = s` and `bias = -s`, so the 2-bit codes `{0, 1, 2}` decode to `-s`, `0`, `+s`. The bias carries no new information, but the container stores two FP16 values per group of 128 where the native format stores one. The effective rate is therefore 2.25 bits/weight against PQ2_0's 2.13. This is a container property, not a different representation: the packed weights decode to exactly the same ternary values as the GGUF bands, verified by comparing the group scales bit for bit. ## Best Practices ### Generation Parameters We recommend using the following sets of sampling parameters for generation: > - Thinking Mode: `temperature=1.0`, `top_p=0.95`, `top_k=20`, `min_p=0.0`, `presence_penalty=0.0`, `repetition_penalty=1.0` > - Instruct (or non-thinking) mode: `temperature=0.7`, `top_p=0.80`, `top_k=20`, `min_p=0.0`, `presence_penalty=1.5`, `repetition_penalty=1.0` These match the base model's own `generation_config.json` and are the values carried in the GGUF metadata (`general.sampling.*`), so a client that reads model defaults will use them without being told. They are also the settings used for the reported benchmark results (thinking mode). The model uses `xhigh` reasoning effort by default; use `medium` for shorter responses and a balance of speed and accuracy. `low` reasoning effort is not supported and when selected the model will behave close to `xhigh`. ### System Prompt You can use a simple system prompt such as: ``` You are a helpful assistant ``` ## Quickstart > **[PrismML-Eng/Bonsai-demo](https://github.com/PrismML-Eng/Bonsai-demo) is the source of truth for > running these models.** It carries the tested setup, builds the right MLX runtime, and is kept > current. Where anything here disagrees with it, it is right. ```bash hf download prism-ml/Ternary-Bonsai-2-27B-mlx-2bit --local-dir bonsai2-27b-mlx ``` This pack declares `model_type: prism_hadamard_qwen35` and **requires the loader bundled in `runtime/`**. Ordinary MLX loaders skip the activation transform and the inverse embedding lookup, so they return wrong output rather than an error. ```bash pip install -r bonsai2-27b-mlx/runtime/requirements.txt ``` ```python import sys sys.path.insert(0, "bonsai2-27b-mlx/runtime") from vision_artifact import load_vl_model, chat_config from mlx_vlm import generate from mlx_vlm.prompt_utils import apply_chat_template model, processor, config = load_vl_model("bonsai2-27b-mlx") prompt = apply_chat_template(processor, chat_config(config), "What is in this image?", num_images=1) print(generate(model, processor, prompt, ["photo.jpg"], max_tokens=256, temperature=1.0, top_p=0.95, top_k=20)) ``` Pass the sampler explicitly: mlx-vlm and mlx-lm do not read `generation_config.json`, so without these arguments generation is greedy. The recommended values are temperature 1.0, top_p 0.95, top_k 20. For a one-command check, `python quickstart.py "your prompt"` in the download directory does all of the above with the sampler and stop tokens taken from `generation_config.json`. Pass no images for text-only use. `chat_config` is needed because mlx-vlm's prompt helper keys off `model_type`. Everything runs on stock packages, no fork; `PACK-RUNTIME.md` documents the contract and [Bonsai-demo](https://github.com/PrismML-Eng/Bonsai-demo) carries the tested setup. For CUDA, CPU, and llama.cpp on Metal, use the GGUF packs of the same weights: **[Ternary-Bonsai-2-27B-gguf](https://huggingface.co/prism-ml/Ternary-Bonsai-2-27B-gguf)**. Those need a build of our [llama.cpp fork](https://github.com/PrismML-Eng/llama.cpp); stock llama.cpp cannot run them. ## Cross-Platform Throughput `tg128` is token-generation throughput over 128 generated tokens (the memory-bandwidth-bound, interactive phase); `pp512` is prompt-processing throughput over 512 input tokens (the compute-bound phase). Both in tokens/s. Rows are measured with llama.cpp (Metal/CUDA, custom low-bit kernels) on the GGUF packs of the same weights, at batch size 1 and depth 0 with no vision tower. NVIDIA energy is board power including HBM/GDDR. | Platform | PQ2_0 TG128 | PQ2_0 PP512 | PQ2_0 J/tok | PTQ1_0 TG128 | PTQ1_0 PP512 | PTQ1_0 J/tok | | :--------------------------- | ----------: | ----------: | ----------: | -----------: | -----------: | -----------: | | RTX 5090 (32 GB) | **129.9** | 3893 | **1.95** | 120.5 | 1805 | 2.15 | | RTX PRO 6000 Blackwell | 124.8 | **4020** | 2.49 | 117.9 | 1972 | 2.77 | | H100 SXM (80 GB) | 113.9 | 2830 | 2.69 | 86.9 | 1237 | 3.18 | | RTX 6000 Ada (48 GB) | 82.8 | 2431 | 2.51 | **90.4** | 1657 | 2.49 | | RTX 4090 (24 GB) | 81.2 | 3124 | 2.99 | **91.1** | 1645 | 2.58 | | L40S (48 GB) | 74.4 | 2868 | 3.24 | **81.8** | 1543 | 2.82 | | A100 SXM (80 GB) | 73.9 | 1328 | 3.43 | 54.7 | 706 | 4.28 | | L4 (24 GB, 72 W) | 29.8 | 777 | 2.42 | **32.1** | 467 | **2.25** | | Laptop (Apple M5 Pro, Metal) | 28.1 | 387 | — | — | — | — | On the laptop the FP16 baseline (\~54 GB) does not fit at all — the meaningful statement is not a speedup ratio but that a 27B model runs interactively on an everyday laptop. The measured decode streams \~204 GB/s of weights on the M5 Pro, confirming the memory-bandwidth-dominated profile that the low-bit representation is built to exploit. The M5 Pro figure is measured on a quiet machine; this laptop swings \~4% with background load. The two packings are a genuine trade rather than a strict ordering. PTQ1_0 moves 17% less weight data per step, but unpacking dense trits costs arithmetic, so it wins on the Ada-generation parts and the L4 — where memory is the binding constraint — and loses on H100, A100, and the Blackwell cards, where batch-1 decode is limited by instruction throughput and launch overhead instead. Prompt processing, being compute-bound, favors PQ2_0 everywhere. The Apple row carries no per-token energy figure because the two platforms' instrumentation does not enclose the same components: `nvidia-smi` includes the card's HBM/GDDR, while Apple's `powermetrics` reports CPU, GPU, and ANE with no DRAM rail. What the measurement does support is absolute draw: the M5 Pro decodes at **27.5 W** on the GPU rail and 34.1 W across CPU and GPU, against 300–455 W of board power for the NVIDIA cards above. ### Additional Apple Platforms Measured on the earlier pre-rotation build and reported pending re-measurement on the current stack (llama.cpp Metal backend): | Platform | Footprint | TG128 (tok/s) | PP512 (tok/s) | | :--------------------------- | --------: | ------------: | ------------: | | Laptop (Apple M5 Max, Metal) | 7.2 GB | 47.0 | 765 | | Laptop (Apple M5 Pro, Metal) | 7.2 GB | 28.7 | 393 | | Laptop (Apple M4 Pro, Metal) | 7.2 GB | 18.0 | 125 | On the wider M5 Max the model reaches \~47 tok/s; on the M4 Pro, prefill (\~125 tok/s) rather than decode is the practical limit for very long prompts. ## Benchmarks Evaluated with EvalScope + vLLM on NVIDIA H100 under identical infrastructure, decoding, and scoring, in **thinking mode** — where the model's full reasoning is exercised and the sub-4-bit collapse of conventional methods is most visible. 14 benchmarks across six skill categories. Bit-widths are true averages; "vs FP16" is relative to the Qwen3.8-27B FP16 reference. | Variant | True bpw | Footprint | Thinking avg | vs FP16 | | :----------------------------------- | -------: | ---------: | -----------: | ---------: | | Qwen3.8-27B FP16 | 16.0 | 54 GB | 86.32 | 100% | | Qwen3.8-27B UD-Q4_K_XL ("4-bit") | 5.2 | 17.6 GB | 85.18 | 98.7% | | Qwen3.8-27B IQ2_XXS ("2-bit") | 2.8 | 9.4 GB | 72.59 | 84.1% | | **Bonsai 2 27B** | **1.72** | **5.9 GB** | **84.78** | **98.2%** | At 5.9 GB, Bonsai 2 27B outscores the sub-4-bit conventional build by more than twelve points at less than two-thirds of its size, and comes within 0.4 points of UD-Q4_K_XL at a third of its footprint. The aggregate gap also understates *how* the conventional builds fail: their degradation is selective, concentrated on the benchmarks that demand sustained chains of reasoning. IQ2_XXS falls to 57.5 on AIME26 and 56.4 on LiveCodeBench while still scoring 88.93 on MMLU-Redux — which is why casual testing misses the collapse. Bonsai 2 holds exactly these benchmarks, scoring 95.83 and 90.07. The previous Bonsai 27B report showed the same pattern on a second model family, Gemma-4-31B, so the collapse is a property of the methods rather than of one base model. ### By Skill Category | Category | Benchmarks | FP16 | Bonsai 2 27B | | :---------------------- | :---------------------------------- | --------: | -----------: | | Knowledge & reasoning | MMLU-Redux, MuSR | 85.55 | 79.86 | | Math | GSM8K, MATH-500, AIME25, AIME26 | 97.06 | 96.57 | | Coding | HumanEval+, MBPP+, LiveCodeBench | 89.07 | 89.42 | | Instruction following | IFEval, IFBench | 81.25 | 82.66 | | Agentic / tool calling | BFCL v3 | 76.74 | 74.92 | | Vision | MMMU-Pro, OCR Bench v2 | 71.36 | 66.19 | | **Overall (14)** | | **86.32** | **84.78** | The reasoning backbone comes through intact: math falls only from 97.06 to 96.57, coding is level with the baseline, and instruction following is slightly ahead of it. The remaining gap is concentrated in the most demanding categories — knowledge and reasoning, and vision. ### Full Per-Benchmark Results