https://llmobile.news/companies/prism-ml/A concise news ticker covering AI on mobile devices, local models, apps and hardware.en-GBhttps://llmobile.news/ticker/bonsai-2-27b/https://llmobile.news/ticker/bonsai-2-27b/Thu, 17 Sep 2026 00:00:00 +0200Prism ML has released Bonsai 2 27B, a compressed version of Qwen3.8 27B that runs on Apple devices including iPhone, iPad and Mac via MLX, using custom low-bit kernels. On NVIDIA GPUs it runs via CUDA. For the iPhone 17 Pro, Prism lists a 1-bit version at 3.9 GB with a 262K-token context window. The main Ternary Bonsai 2 27B model stores each weight as -1, 0 or +1 with FP16 scaling per group of weights. This works out to 1.76 effective bits per weight and a total footprint of 5.9 GB, which Prism says is more than 9x smaller than the full-precision model. The model accepts text and image input. Prism reports an aggregate benchmark score of 83.9 for the ternary model, compared with 85.4 for Qwen3.8 27B, which it describes as 98.2% retention. The model scores 82.66 in instruction following against 81.25 for the original, and trails it in knowledge and reasoning (83.95 against 86.66), vision (78.59 against 81.64) and agentic tool calling (77.57 against 79.74). Prism also gives 96.57 for math and 81.58 for coding, against 97.06 and 82.17 for Qwen3.8 27B. According to Prism, the ternary model generates up to 143 tok/s on an NVIDIA GeForce RTX 5090 and 46.8 tok/s on an Apple M5 Max. On an RTX 4090 it uses 0.714 mWh per token, which the company says is 40% less than a full-precision 8B model. The project repository lists peak memory for text-only use at about 7.8 GB at 4K context and 13.7 GB at 100K context, with images adding about 0.9 GB. The model is released under the Apache 2.0 license. The Hugging Face collection holds GGUF and MLX 2-bit versions, and the repository documents running it with llama.cpp on Macs with Apple silicon and on CUDA, Vulkan, ROCm and CPU. A WebGPU demo runs in the browser on Hugging Face Spaces. Source: https://prismml.com/news/bonsai-2-27b Read the article: https://llmobile.news/ticker/bonsai-2-27b/ModelsAppleiPhonehttps://llmobile.news/ticker/bonsai-27b/https://llmobile.news/ticker/bonsai-27b/Tue, 14 Jul 2026 00:00:00 +0200Prism ML has released Bonsai 27B, a compressed version of Qwen3.6 27B for Mac, iPhone, iPad and NVIDIA GPUs. The 1-bit variant needs 3.9 GB, which Prism says fits within the memory budget of an iPhone 17 Pro. The company notes that a phone never exposes its full memory to an app and that a 12 GB iPhone offers about 6 GB to a model. The ternary variant stores each weight as -1, 0 or +1 and takes up 5.9 GB at 1.71 effective bits per weight. The 1-bit variant stores weights as -1 or +1 at 1.125 effective bits. Prism applies the low-bit format to embeddings, attention layers, MLPs and the output head. Both models accept images through a 4-bit vision tower and offer a 262K-token context window. Across a 15-benchmark suite, Prism reports an overall score of 85.0 for Qwen3.6 27B, 80.5 for the ternary model and 76.1 for the 1-bit model, which it describes as retaining 95% and 90% of the original. The largest gaps show up in agentic tool calling, at 80.0 for the original, 74.0 for ternary and 66.0 for 1-bit, and in vision, at 72.6, 65.2 and 59.6. Prism also publishes an “intelligence density” figure, defined as the negative log of the error rate divided by model size in GB, of 0.530 for the 1-bit model and 0.400 for the ternary one. On an NVIDIA GeForce RTX 5090, Prism measures 163 tok/s for the 1-bit model and 134 tok/s for the ternary model. On an Apple M5 Max the figures are 87 and 58 tok/s. The models also support speculative decoding, where a small draft model proposes tokens that the main model then verifies. Bonsai 27B is released under the Apache 2.0 license, with builds for MLX on Apple devices and for CUDA on NVIDIA GPUs. Weights are in the Hugging Face collection, and the Bonsai-demo repository covers running them. Prism also lists the Locally AI iOS app and a free, limited-time developer API on Together.ai as ways to try the model. Source: https://prismml.com/news/bonsai-27b Read the article: https://llmobile.news/ticker/bonsai-27b/QuantisationBenchmarksAppleOpen sourcehttps://llmobile.news/ticker/bonsai-image-4b/https://llmobile.news/ticker/bonsai-image-4b/Tue, 26 May 2026 00:00:00 +0200Prism ML has released Bonsai Image 4B, an image generation model with 4 billion parameters in a 1-bit and a ternary version. The company says it is the first image model in its parameter class to run directly on an iPhone. It generates a 512x512 image in 9.4 seconds on an iPhone 17 Pro Max and in about 6 seconds on an M4 Pro Mac, where Prism reports up to 5.6 times the speed of the stock full-precision MFLUX pipeline. Sample images from the Bonsai Image 4B announcement.Prism ML The diffusion transformer takes 0.93 GB in the 1-bit version, which uses 1.125 effective bits per weight, and 1.21 GB in the ternary version at 1.71 bits. Prism lists 7.75 GB for the comparison model FLUX.2 Klein 4B. The full deployment payload on Apple Silicon is 3.42 GB and 3.88 GB, against 15.97 GB for FLUX.2 Klein 4B. At runtime, 512x512 generation uses 1.5 GB and 1.96 GB against 11.74 GB, and 1024x1024 generation uses 1.95 GB and 2.38 GB against 14.39 GB. Prism scores the ternary version at 0.723 on GenEval, 12.22 on HPSv3 and 0.851 on DPG-Bench, which it summarizes as 95% of FLUX.2 Klein 4B overall. The 1-bit version scores 0.671, 11.15 and 0.822, or 88%, while FLUX.2 Klein 4B scores 0.819, 12.84 and 0.853. GenEval measures object composition and attribute binding, HPSv3 human preference and DPG-Bench how well dense prompts are followed. The models run on iPhone, iPad and Mac via MLX low-bit paths and on CUDA GPUs via Gemlite low-bit kernels. They are released as open weights and code under the Apache 2.0 license, and Prism ships the iOS app Bonsai Studio for trying the model on iPhone. Weights are in the Hugging Face collection, code is in the GitHub repository, and a WebGPU demo runs in the browser. Source: https://prismml.com/news/bonsai-image-4b Read the article: https://llmobile.news/ticker/bonsai-image-4b/QuantisationAppleDemosOpen sourcehttps://llmobile.news/ticker/ternary-bonsai-8b/https://llmobile.news/ticker/ternary-bonsai-8b/Thu, 16 Apr 2026 00:00:00 +0200Prism ML has released Ternary Bonsai, a model family in 8B, 4B and 1.7B sizes that stores weights as -1, 0 or +1 instead of a single bit. Ternary Bonsai 8B needs 1.75 GB, which Prism says is about 9 times smaller than a standard 16-bit model. The scheme works out to 1.58 bits per weight, with each group of 128 weights sharing an FP16 scale factor, and it applies to the entire architecture. Prism reports 82 tok/s on an M4 Pro and 27 tok/s on an iPhone 17 Pro Max for the 8B model. A 4-bit 8B model reaches 13.8 tok/s on the same iPhone in the company’s chart. Energy use is 0.105 mWh per token on the M4 Pro and 0.132 mWh per token on the iPhone 17 Pro Max, which Prism says is 3 to 4 times better than 16-bit counterparts. Ternary Bonsai 8B averages 75.5 across Prism’s benchmark set, compared with 70.5 for the earlier 1-bit Bonsai 8B, at about 600 MB more memory. The individual scores are 72.6 on MMLU Redux, 56.2 on MuSR, 91.0 on GSM8K, 77.4 on HumanEval+, 81.8 on IFEval and 73.9 on BFCLv3. According to Prism, only Qwen3 8B (16.38 GB) scores higher among the 8B-class models it compared, and the Bonsai model is 9 to 10 times smaller. Average benchmark score against model size, as charted by Prism ML.Prism ML The models are released under the Apache 2.0 license. They are available on Hugging Face and in the GitHub repository, which covers macOS, and run on Apple devices via MLX. On iPhone the iOS app Locally AI runs them, and a whitepaper describes the method. Source: https://prismml.com/news/ternary-bonsai Read the article: https://llmobile.news/ticker/ternary-bonsai-8b/QuantisationBenchmarksiOSOpen sourcehttps://llmobile.news/ticker/1-bit-bonsai-8b/https://llmobile.news/ticker/1-bit-bonsai-8b/Tue, 31 Mar 2026 00:00:00 +0200Prism ML has released 1-bit Bonsai 8B, a language model with 8.2 billion parameters that stores every weight in a single bit. Embeddings, attention layers, MLP layers and the LM head are all 1-bit, which brings the model down to 1.15 GB. The company also released 4B and 1.7B versions of the family. On Apple hardware Prism reports about 40 tok/s on an iPhone 17 Pro and about 44 tok/s on an iPhone 17 Pro Max, against 131 tok/s on an M4 Pro Mac. An NVIDIA RTX 4090 reaches 368 tok/s, compared with 59 tok/s for a 16-bit 8B model in Prism’s chart. The 16-bit model reaches 16 tok/s on the M4 Pro and does not fit on the iPhone 17 Pro Max. Prism measured 0.074 mWh per token on the M4 Pro and 0.068 mWh per token on the iPhone 17 Pro Max, which it puts at 4 to 5 times less than 16-bit counterparts. On the RTX 4090 the figures are 0.276 mWh for 1-bit Bonsai 8B and 1.134 mWh for the 16-bit model. Energy per token for 1-bit Bonsai 8B and a 16-bit 8B model, as measured by Prism ML.Prism ML The company gives the 8B model an average benchmark score of 70.5, made up of 65.7 on MMLU Redux, 50.0 on MuSR, 88.0 on GSM8K, 73.8 on HumanEval+, 79.8 on IFEval and 65.7 on BFCLv3. It compares the model with 8B-class models such as Qwen3 8B, Llama 3.1 8B, Ministral3 8B and LFM2 8B. On its own “intelligence density” metric, a score per GB of model size, Prism lists 1.06 for Bonsai 8B and 0.10 for Qwen3 8B. The models are released under the Apache 2.0 license. They run on Apple devices via MLX and on NVIDIA GPUs via llama.cpp CUDA, and the iOS app Locally AI can run them on iPhone. Weights are in the Hugging Face collection, and the demo repository holds a whitepaper and quick start instructions, along with a Colab notebook. Source: https://prismml.com/news/bonsai-8b Read the article: https://llmobile.news/ticker/1-bit-bonsai-8b/QuantisationBenchmarksAppleOpen source