# PrismML

> Source: https://aiwiki.ai/wiki/prism_ml
> Updated: 2026-10-30
> Fact-checked: 2026-10-30
> Categories: AI Companies, AI Inference, Open Source AI, Small Language Models
> License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) - attribute to "AI Wiki (aiwiki.ai)"
> Cite as: AI Wiki. "PrismML." aiwiki.ai, 30 Oct 2026. https://aiwiki.ai/wiki/prism_ml
> From AI Wiki (https://aiwiki.ai), the free encyclopedia of artificial intelligence. Reuse freely with attribution.

**PrismML** (also styled **Prism ML**; legal name Prism ML, Inc.) is an American artificial intelligence company, based in Pasadena, California, that publishes extremely low-bit versions of open-weight models under the name **Bonsai**.[1][16] Its models store almost every weight of the network as either a sign bit (1-bit, values {-1, +1}) or a ternary digit (values {-1, 0, +1}, about 1.58 bits of information), each group of 128 weights sharing one 16-bit scale factor.[19][21][22] The company came out of stealth on 31 March 2026 with 1-bit Bonsai 8B and grew out of research by Caltech electrical engineering professor Babak Hassibi, its co-founder and chief executive.[1][3][4] As of 30 October 2026 its flagship is Ternary Bonsai 2 27B, a 5.95 GB ternary build derived from [Qwen3.8](https://aiwiki.ai/wiki/qwen3_8)-27B that PrismML says keeps 98.2% of the original model's benchmark average.[8][19]

All Bonsai weights are released on [Hugging Face](https://aiwiki.ai/wiki/hugging_face) under the [Apache License 2.0](https://aiwiki.ai/wiki/apache_license).[1][19] Several of the packing formats need PrismML's own kernels, distributed through forks of [llama.cpp](https://aiwiki.ai/wiki/llama_cpp) and Apple's [MLX](https://aiwiki.ai/wiki/mlx), although parts of that support have since been merged into mainline llama.cpp.[19][30][31] The benchmark results in PrismML's announcements come from the company's own evaluations.[8][20]

## History

### Stealth exit and the 1-bit family (March 2026)

PrismML announced itself on 31 March 2026 with a press release dated from Pasadena. It said the company was introducing "the world's first commercially viable 1-bit large language models, built on groundbreaking research developed at Caltech", led by 1-bit Bonsai 8B, and released 1-bit Bonsai 4B and 1.7B alongside it.[1] Hassibi was quoted saying the team "spent years developing the mathematical theory required to compress a neural network without losing its reasoning capabilities".[1] The company said the 8B model had been trained using Google v4 TPUs.[1]

The Register covered the launch on 4 April 2026. It reported that PrismML's approach is based on work by Hassibi and colleagues at Caltech, and placed it in a longer line of low-bit research that includes the 2024 paper "The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits".[3] On 1 April 2026 a pull request written on behalf of the Bonsai team ("We just released Bonsai 1-bit models") proposed adding the `Q1_0` format to mainline llama.cpp; it was merged on 6 April 2026.[30]

### Ternary and image models (April to May 2026)

On 16 April 2026 PrismML announced the Ternary Bonsai family (8B, 4B and 1.7B), which uses three weight values instead of two. The company said Ternary Bonsai 8B averaged 5 benchmark points more than 1-bit Bonsai 8B while needing about 600 MB more memory.[9][10] On 26 May 2026 it released Bonsai Image 4B, 1-bit and ternary versions of the FLUX.2 Klein 4B image model (part of the [FLUX.2](https://aiwiki.ai/wiki/flux_2) family), together with an iOS app called Bonsai Studio.[11][12]

### Bonsai 27B and Apple (July 2026)

On 14 July 2026 PrismML released Bonsai 27B, derived from [Qwen3.6](https://aiwiki.ai/wiki/qwen3_6)-27B, in a 1-bit variant of about 3.9 GB and a ternary variant.[13][14] PrismML's press release said the 1-bit variant ran at 11 tokens per second on an iPhone 17 Pro; its model card gives the figure for an iPhone 17 Pro Max.[13][23] The same day CNBC reported that [Apple](https://aiwiki.ai/wiki/apple) was in talks with the company. Hassibi told CNBC that Apple and other companies had been evaluating PrismML's models for speed, energy efficiency and on-device performance; he called the discussions very early but said "things are progressing nicely". Apple did not immediately respond to CNBC's request for comment.[5][34] The Next Web added that The Information had first reported Apple's interest the week before.[34] 9to5Mac wrote that it was "clear" PrismML was "touting communication with Apple as a way to generate buzz" around the release.[33] Hassibi told CNBC that Google's [Gemma](https://aiwiki.ai/wiki/gemma) models were next in the pipeline, followed by much larger models.[5] Summarising the accompanying whitepaper, The Decoder reported a battery test of roughly 672 generated tokens per percentage point of charge for the 1-bit model on an iPhone 17 Pro Max.[32]

### Bonsai 2 (September to October 2026)

PrismML released Ternary Bonsai 2 27B, based on Qwen3.8-27B, on 17 September 2026.[8][15][35] TechCrunch's coverage that day described PrismML as "founded by a group of Caltech researchers" and quoted Hassibi saying the company's next models, "hopefully in the next couple of months, will be in the several-hundred-billion-parameter range".[6] At [Qualcomm](https://aiwiki.ai/wiki/qualcomm)'s Snapdragon Summit on 23 September 2026, a 2-billion-parameter vision-language model built on Bonsai 1.7B was shown running on smart glasses that use the Snapdragon AR1 Gen 1 platform.[16][7] TechCrunch noted that no smart glasses running PrismML's model had been announced.[7] On 15 October 2026 the company added Ternary Bonsai 2 2B, 4B and 9B, based on [Qwen3.5](https://aiwiki.ai/wiki/qwen3_5).[17][18]

## People and funding

The company's "Meet the team" page lists four co-founders:[4]

| Name | Role listed by PrismML |
|---|---|
| Babak Hassibi | Co-founder and CEO |
| Sahin Lale | Co-founder and co-head of research |
| Omead Pooladzandi | Co-founder and co-head of research |
| Reza Sadri | Co-founder and VP, strategy |

The same page names [Ion Stoica](https://aiwiki.ai/wiki/ion_stoica), Buno Pati and Julie Schoenfeld as advisors.[4] TechCrunch describes Stoica, a co-founder of [Databricks](https://aiwiki.ai/wiki/databricks) and director of UC Berkeley's Sky Computing Lab, as an adviser to the company.[6]

Hassibi is a professor of electrical engineering at Caltech.[3] He is the first author, with David G. Stork, of "Second Order Derivatives for Network Pruning: Optimal Brain Surgeon" (NIPS 1992), an early paper on removing unimportant weights from trained neural networks (see [pruning](https://aiwiki.ai/wiki/pruning)).[37] CNBC reported that the Bonsai technology emerged from Hassibi's research group and that Caltech owns the underlying patents and licenses them exclusively to PrismML.[5]

Reported funding figures differ by date. CNBC and The Next Web reported in July 2026 that PrismML raised a $16.25 million seed round in March, backed by [Khosla Ventures](https://aiwiki.ai/wiki/khosla_ventures) and other investors.[5][34] TechCrunch described a $22.25 million seed round in September 2026.[6] PrismML's March press release said the company was backed by Khosla Ventures and Cerberus Ventures, with compute grants from [Google](https://aiwiki.ai/wiki/google) and Caltech.[1] Its later press releases say it "was founded with support from Khosla Ventures, Cerberus, and Google, with continuing support from [Samsung](https://aiwiki.ai/wiki/samsung)"; the 23 September release adds Gates Frontier and [Jeff Dean](https://aiwiki.ai/wiki/jeff_dean) to that list.[8][16]

## Technology

### Weight formats

Every Bonsai language model uses group-wise scaling: weights are split into groups of 128, and each group stores one FP16 scale. In the 1-bit format a stored 0 means minus the scale and a stored 1 means plus the scale, which costs 1 bit plus 16/128 bits, or 1.125 bits per weight.[21] In the ternary format each weight is minus the scale, zero, or plus the scale. A ternary value carries log2(3), about 1.585 bits of information, so the effective cost is about 1.71 bits per weight once the scale is included.[14][19]

PrismML stresses that the low-bit representation covers the embeddings, attention projections, MLP projections and the output (LM) head, with "no higher-precision escape hatches".[2][10] In Bonsai 2 27B a small set of tensors (the recurrent-state path of the linear-attention layers and the normalization weights, 26.2 million parameters or 0.0976% of the language model) is kept in higher precision, which raises the whole-model figure to 1.72 bits per weight.[19][20] Bonsai 2 also stores weights in a rotated basis: each matrix is transformed blockwise by a fixed Walsh-Hadamard rotation (block size 1024, with fixed sign flips) before the ternary values are assigned, and the runtime applies the matching transform to activations. The whitepaper cites [SpinQuant](https://aiwiki.ai/wiki/spinquant) for this idea.[19][20]

Bonsai models keep the architecture of the model they are derived from. The model cards say Bonsai 2 27B is "derived from Qwen3.8-27B" with the "architecture unchanged", and the 2B, 4B and 9B cards say the same of their Qwen3.5 bases.[19][25] The cards and whitepapers document the stored formats and the inference kernels; PrismML calls the underlying design proprietary, and its press releases say the 27B and Bonsai 2 models were trained on Google v5 TPUs.[2][9][8][13][17]

### Relation to BitNet and conventional quantization

The {-1, 0, +1} alphabet is the same one used by Microsoft's [BitNet b1.58](https://aiwiki.ai/wiki/bitnet_b1_58), whose authors present ternary weights as "a new scaling law and recipe for training new generations of LLMs" (see [BitNet](https://aiwiki.ai/wiki/bitnet) and [quantization-aware training](https://aiwiki.ai/wiki/quantization_aware_training)).[39] The Register cited that paper as prior work when covering PrismML's launch.[3] Bonsai instead starts from existing open-weight checkpoints from Alibaba's [Qwen](https://aiwiki.ai/wiki/qwen) family and, for images, FLUX.2 Klein 4B.[12][19][21] When PrismML proposed a 2-bit ternary format for llama.cpp, it explained why it did not reuse llama.cpp's existing ternary types `TQ1_0` and `TQ2_0`: those use groups of 256 weights rather than 128 and were CPU-only and harder to accelerate on Metal and CUDA.[31]

PrismML compares Bonsai against conventional post-training [quantization](https://aiwiki.ai/wiki/quantization) builds of the same base model, such as the IQ2_XXS and UD-Q4_K_XL [GGUF](https://aiwiki.ai/wiki/gguf) files.[19] The Bonsai 27B model card argues that conventional labels understate real bit-widths, citing a "2-bit" build of Qwen3.6-27B that averages 2.8 bits per weight at 9.4 GB.[23] The company also says conventional low-bit builds fail unevenly: in its whitepaper's words, "long context reasoning and coding deteriorate first, while surface fluency and short tool calls retain high performance".[19][20]

### Intelligence density

PrismML promotes a metric it calls intelligence density: the negative logarithm of a model's average error rate on a benchmark suite, divided by the model's size in gigabytes.[2][3] The Register described it as "a metric that shows its models in a good light".[3] The definition is not applied uniformly: the 1-bit Bonsai 8B card uses the natural logarithm, while the Bonsai 2 27B card and whitepaper use log base 2.[19][20][21]

## Models

The table lists PrismML's published models with the base model and sizes given on each model card or announcement. Release dates are announcement dates; some Hugging Face repositories were created days or weeks earlier.[28]

| Model | Announced | Base model | Weights | Language-model size (vendor figure) |
|---|---|---|---|---|
| 1-bit Bonsai 8B | 31 Mar 2026 | Qwen3-8B architecture | 1-bit, `Q1_0` | 1.15 GB[21] |
| 1-bit Bonsai 4B / 1.7B | 31 Mar 2026 | Qwen3-4B / Qwen3-1.7B architecture | 1-bit, `Q1_0` | 0.57 GB / 0.24 GB[26][27] |
| Ternary Bonsai 8B / 4B / 1.7B | 16 Apr 2026 | Qwen3-8B / 4B / 1.7B | Ternary, packed as `Q2_0` | 2.18 GB / 1.07 GB / 0.46 GB packed[22][9] |
| 1-bit and Ternary Bonsai Image 4B | 26 May 2026 | FLUX.2 Klein 4B | 1-bit (1.125 bpw) / ternary (1.71 bpw) diffusion transformer | Transformer 0.93 GB / 1.21 GB[11][12] |
| 1-bit Bonsai 27B | 14 Jul 2026 | Qwen3.6-27B | 1-bit, `Q1_0` | About 3.9 GB[23] |
| Ternary Bonsai 27B | 14 Jul 2026 | Qwen3.6-27B | Ternary, 2-bit slots | About 7.2 GB deployed (5.9 GB ideal)[24] |
| Ternary Bonsai 2 27B | 17 Sep 2026 | Qwen3.8-27B | Ternary, `PTQ1_0` or `PQ2_0` | 5.95 GB / 7.21 GB[19] |
| Ternary Bonsai 2 9B / 4B / 2B | 15 Oct 2026 | Qwen3.5-9B / 4B / 2B | Ternary, `PTQ1_0`, `PQ2_0` or `Q2_0` | 2.00 GB / 0.95 GB / 0.43 GB[17] |

The ternary 8B family's announcement gives a smaller footprint (1.75 GB for the 8B) than the packed `Q2_0` file listed on its model card (2.18 GB), because the 2-bit packing stores each three-valued weight in a 2-bit slot.[10][22] Bonsai 27B and later models accept images as well as text; the vision tower ships as a separate optional file.[14][19] For the smart-glasses demonstration PrismML built a separate 2B vision-language model from Bonsai 1.7B and a 4-bit vision encoder.[16]

## Inference software

PrismML maintains forks of llama.cpp (default branch `prism`), MLX and mlx-swift, plus a `Bonsai-demo` repository that its model cards call "the source of truth for running these models".[19][29] Much of the low-bit support has since moved into mainline llama.cpp. The `Q1_0` format (1-bit, groups of 128) was merged for CPU on 6 April 2026, and a CUDA backend followed on 15 April.[30][38] A `Q2_0` ternary format was merged in July 2026. At the maintainers' request it uses groups of 64, so PrismML keeps a group-of-128 variant called `PQ2_0` in its fork.[31]

The Bonsai 2 27B files still need the fork. Its model card says "Stock llama.cpp will not run these files": mainline rejects `PQ2_0` and `PTQ1_0` as unknown types.[19] The repository ships no `Q2_0` file, and its known-issues page says the `F16` file loads in stock llama.cpp but produces garbled output because it depends on metadata that only the PrismML build applies. PrismML's October cards for the smaller Bonsai 2 models say recent mainline builds include the Hadamard activation runtime and can run their `Q2_0` files.[19][25] `PTQ1_0` packs ternary digits densely (1.75 bits per weight, 5.95 GB), while `PQ2_0` gives each digit a 2-bit slot (2.13 bits per weight, 7.21 GB). PrismML reports that neither is faster everywhere: for decoding, `PTQ1_0` wins on Ada-generation GPUs and the L4, and `PQ2_0` wins on H100, A100 and Blackwell cards.[19]

On GitHub, the `Bonsai-demo` repository had 3,467 stars and the llama.cpp fork 986 stars as of 30 October 2026.[29]

## Bonsai 2 27B

Ternary Bonsai 2 27B has 27.36 billion parameters: a 24.35-billion-parameter language backbone of 64 blocks, 2.54 billion in the embedding and LM head, and a 0.46-billion-parameter vision tower. Like its base model, it uses hybrid attention (about 75% linear attention and 25% full attention) and a 262K-token context window.[19] The FP16 original is about 54 GB.[19]

### Vendor benchmarks

PrismML reports two different benchmark suites for the model, both run in thinking mode with EvalScope and [vLLM](https://aiwiki.ai/wiki/vllm) on NVIDIA H100 GPUs:[19][20]

| Suite | Qwen3.8-27B FP16 | Ternary Bonsai 2 27B | Qwen3.8-27B IQ2_XXS | Retention |
|---|---|---|---|---|
| 14 benchmarks (model card) | 86.32 | 84.78 | 72.59 | 98.2% |
| 20 benchmarks (whitepaper and press release) | 85.4 | 83.9 | 75.2 | 98.2% |

The 14-benchmark card suite covers MMLU-Redux, MuSR, GSM8K, MATH-500, AIME25, AIME26, HumanEval+, MBPP+, LiveCodeBench, IFEval, IFBench, BFCL v3, MMMU-Pro and OCR Bench v2. On it the conventional UD-Q4_K_XL build (17.6 GB) scores 85.18.[19] The 20-benchmark whitepaper suite drops MuSR and MMMU-Pro and adds GPQA Diamond, AA-LCR, BigCodeBench, τ²-Bench, CharXiv, A-OKVQA, OmniDocBench v1.6 and RealWorldQA.[20] In the whitepaper PrismML also reports 52.8 on [Terminal-Bench](https://aiwiki.ai/wiki/terminal_bench) 2.1 and 60.8 on [SWE-bench Verified](https://aiwiki.ai/wiki/swe_bench_verified), against 69.7 and 80.6 for the full-precision Qwen3.8-27B, or roughly three quarters of full-precision performance.[20] By category, the card shows the largest losses in knowledge and reasoning (79.86 against 85.55) and vision (66.19 against 71.36).[19]

The vendor's size and speed figures also vary between documents. The model card gives 5.95 GB and a reduction of about 9.0x for the `PTQ1_0` file, while the whitepaper gives 5.93 GB and about 9.1x against a 53.80 GB FP16 baseline.[19][20] In the card's `llama-bench` measurements (batch size 1, 128 generated tokens), decode speed is 129.9 tokens per second on an RTX 5090 with `PQ2_0` (120.5 with `PTQ1_0`) and 28.1 tokens per second on an Apple M5 Pro laptop.[19] The press release gives up to 143 tokens per second on the RTX 5090, which matches the whitepaper's 142.5 tokens per second for `PQ2_0` (134.4 for `PTQ1_0`, and 27.7 on the M5 Pro).[8][20]

### Memory beyond the weights

The 5.95 GB figure covers the language model's weights only. PrismML's own Bonsai 27B model card explains that a device must hold peak memory: the weights plus the [KV cache](https://aiwiki.ai/wiki/kv_cache), activations and runtime buffers (about 1.3 GB across backends). For 1-bit Bonsai 27B it measured 3.79 GB of weights, 5.2 GB peak at a 4K-token context and 11.6 GB at 100K tokens with an FP16 cache, and lower peaks with a 4-bit cache.[23] The company's Bonsai 27B announcement makes the same point about phones: a 12 GB iPhone offers an app about 6 GB, which the model shares with its KV cache and activations.[14]

## Reception and evaluation

Coverage has mostly relayed PrismML's own numbers. CNBC reported that analysts cautioned the claims "still need to be proven outside controlled demonstrations". Tarun Pathak of Counterpoint Research said "the ultimate test will be millions of queries, thousands of device combinations and robust testing at scale", and Phil Solis of IDC said power consumption may be the biggest open question.[5] The Register noted that Qwen3 8B scored somewhat ahead of 1-bit Bonsai 8B on the company's benchmark list.[3] Hassibi told CNBC that the models typically lose a few percentage points of overall performance, with factual recall degrading before reasoning, math and coding.[5]

At least one competitor has published a comparison. Underdog (Conway Research), which publishes its own open-weight 2-bit builds of Qwen3.8-27B, ran Ternary Bonsai 2 27B (`PTQ1_0`) through its tests at launch of its [Underdog Saluki 27B](https://aiwiki.ai/wiki/underdog_saluki_27b). On Underdog's 120-task benchmark the Bonsai build passed 70 tasks against 84 for full-size Qwen3.8-27B and 88 for Saluki. On 100 parallel tool-calling tasks from the Berkeley Function Calling Leaderboard it passed 11 against 35 for the full-size model, and on a 50-issue SWE-bench Verified subset it fixed 29 against 33.[36] These are a rival vendor's own measurements.

On Hugging Face, the `Ternary-Bonsai-2-27B-gguf` repository recorded 1,953,832 downloads in the 30 days to 30 October 2026 and 5,708,739 in total; the 42 repositories under the `prism-ml` account recorded about 24.6 million downloads in total.[28]

## See also

- [BitNet](https://aiwiki.ai/wiki/bitnet)
- [Quantization](https://aiwiki.ai/wiki/quantization)
- [Model compression](https://aiwiki.ai/wiki/model_compression)
- [On-device AI](https://aiwiki.ai/wiki/on_device_ai)
- [Edge AI](https://aiwiki.ai/wiki/edge_ai)

## References

1. Prism ML. "PrismML Launches World's First 1-Bit AI Model to Redefine Intelligence at the Edge" (press release). Pasadena, 31 March 2026. https://prismml.com/news/prismml-launches-worlds-first-1-bit-ai-model (accessed 30 October 2026).
2. Prism ML. "Announcing 1-bit Bonsai: The First Commercially Viable 1-bit LLMs" (blog). 31 March 2026. https://prismml.com/news/bonsai-8b (accessed 30 October 2026).
3. Claburn, Thomas. "PrismML debuts energy-sipping 1-bit LLM in bid to free AI from the cloud." The Register, 4 April 2026. https://www.theregister.com/2026/04/04/prismml_1bit_llm/
4. Prism ML. "About" (team page) and careers listings. https://prismml.com/about (accessed 30 October 2026).
5. Sigalos, MacKenzie. "Apple in talks with startup that shrinks AI models to run on an iPhone." CNBC, 14 July 2026. https://www.cnbc.com/2026/07/14/apple-prismml-ai-compression-iphone.html
6. Bort, Julie. "PrismML hopes its tiny LLM will change how we all use AI." TechCrunch, 17 September 2026. https://techcrunch.com/2026/09/17/prismml-hopes-its-tiny-llm-could-change-how-we-all-use-ai/
7. Bort, Julie. "PrismML brings its tiny LLMs to Qualcomm-powered smart glasses." TechCrunch, 24 September 2026. https://techcrunch.com/2026/09/24/prismml-brings-its-tiny-llms-to-qualcomm-powered-smart-glasses/
8. PrismML. "PrismML Launches Bonsai 2 27B, Its Most Capable Model Yet" (press release via PR Newswire). 17 September 2026. https://www.morningstar.com/news/pr-newswire/20260917sf50707/prismml-launches-bonsai-2-27b-its-most-capable-model-yet
9. Prism ML. "PrismML Introduces Ternary Bonsai Model Family" (press release). Pasadena, 16 April 2026. https://prismml.com/news/prismml-introduces-ternary-bonsai-model-family (accessed 30 October 2026).
10. Prism ML. "Introducing Ternary Bonsai: Top Intelligence at 1.58 Bits" (blog). April 2026. https://prismml.com/news/ternary-bonsai (accessed 30 October 2026).
11. Prism ML. "PrismML Releases Bonsai Image 4B" (press release). Pasadena, 26 May 2026. https://prismml.com/news/prismml-releases-bonsai-image-4b (accessed 30 October 2026).
12. Prism ML. "Introducing 1-bit and Ternary Bonsai Image 4B: Image Generation for Local Devices" (blog). May 2026. https://prismml.com/news/bonsai-image-4b (accessed 30 October 2026).
13. Prism ML. "PrismML Announces 1-bit Bonsai 27B: The First 27B Model to Run on a Phone" (press release). Pasadena, 14 July 2026. https://prismml.com/news/prismml-releases-bonsai-27b (accessed 30 October 2026).
14. Prism ML. "Announcing Bonsai 27B: The First 27B-Class Model to Run on a Phone" (blog). July 2026. https://prismml.com/news/bonsai-27b (accessed 30 October 2026).
15. Prism ML. "Introducing Bonsai 2 27B: Near-Lossless Compression in a 9x Smaller Footprint" (blog). September 2026. https://prismml.com/news/bonsai-2-27b (accessed 30 October 2026).
16. Prism ML. "PrismML Brings 1-Bit Bonsai Models to AI Smart Glasses Powered by Snapdragon" (press release). Pasadena, 23 September 2026. https://prismml.com/news/prismml-brings-1-bit-bonsai-models-to-ai-smart-glasses-powered-by-snapdragon (accessed 30 October 2026).
17. Prism ML. "PrismML Expands Bonsai 2 Family with 2B, 4B, and 9B Models for On-Device AI" (press release). Pasadena, 15 October 2026. https://prismml.com/news/prismml-expands-bonsai-2-family-with-2b-4b-and-9b-models-for-on-device-ai (accessed 30 October 2026).
18. Prism ML. "Introducing Ternary Bonsai 2 2B, 4B, and 9B: Bringing Local Agents to Everyday Devices" (blog). October 2026. https://prismml.com/news/bonsai-2-family (accessed 30 October 2026).
19. prism-ml. "Bonsai 2 27B: GGUF" (model card, Ternary-Bonsai-2-27B-gguf). Hugging Face. https://huggingface.co/prism-ml/Ternary-Bonsai-2-27B-gguf (accessed 30 October 2026).
20. PrismML. "Ternary Bonsai 2 27B: A 27B ternary reasoning model" (whitepaper). September 2026. https://github.com/PrismML-Eng/Bonsai-demo/blob/main/bonsai-2-27b-whitepaper.pdf (accessed 30 October 2026).
21. prism-ml. "Bonsai-8B-GGUF-1bit" (model card). Hugging Face. https://huggingface.co/prism-ml/Bonsai-8B-gguf (accessed 30 October 2026).
22. prism-ml. "Ternary-Bonsai-8B-gguf" (model card); see also the Ternary-Bonsai-4B-gguf and Ternary-Bonsai-1.7B-gguf cards. Hugging Face. https://huggingface.co/prism-ml/Ternary-Bonsai-8B-gguf (accessed 30 October 2026).
23. prism-ml. "1-bit Bonsai 27B: GGUF" (model card, Bonsai-27B-gguf). Hugging Face. https://huggingface.co/prism-ml/Bonsai-27B-gguf (accessed 30 October 2026).
24. prism-ml. "Ternary Bonsai 27B: GGUF" (model card). Hugging Face. https://huggingface.co/prism-ml/Ternary-Bonsai-27B-gguf (accessed 30 October 2026).
25. prism-ml. "Bonsai 2 Ternary 9B: GGUF" (model card); see also the 4B and 2B cards. Hugging Face. https://huggingface.co/prism-ml/Ternary-Bonsai-2-9B-gguf (accessed 30 October 2026).
26. prism-ml. "Bonsai-4B-GGUF-1bit" (model card). Hugging Face. https://huggingface.co/prism-ml/Bonsai-4B-gguf (accessed 30 October 2026).
27. prism-ml. "Bonsai-1.7B-GGUF-1bit" (model card). Hugging Face. https://huggingface.co/prism-ml/Bonsai-1.7B-gguf (accessed 30 October 2026).
28. Hugging Face. Model API listing for the prism-ml account (downloads, downloadsAllTime, createdAt). https://huggingface.co/api/models?author=prism-ml (accessed 30 October 2026).
29. GitHub. Repository records for PrismML-Eng/Bonsai-demo and PrismML-Eng/llama.cpp. https://github.com/PrismML-Eng/Bonsai-demo and https://github.com/PrismML-Eng/llama.cpp (accessed 30 October 2026).
30. khosravipasha. "ggml: add Q1_0 1-bit quantization support (CPU)." llama.cpp pull request #21273, merged 6 April 2026. https://github.com/ggml-org/llama.cpp/pull/21273
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