Tagged "prismml"
11 articles tagged prismml, 1 April 2026 to 19 September 2026. Newest first.
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PrismML Releases Ternary Bonsai 2 27B: 5.9 GB Model Retaining 98.2% Performance
PrismML releases a heavily quantized 27B parameter model in just 5.9 GB while maintaining 98.2% of the original Qwen3.8 27B performance, demonstrating breakthrough compression for edge deployment.
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Apple's On-Device AI Strategy Focuses on Privacy and Latency, Not ChatGPT Competition
Apple's approach to on-device AI with PrismML prioritizes privacy, latency, and local execution over competing with cloud LLMs. The strategy highlights how Apple Silicon hardware is fundamentally changing what's possible for edge inference and private AI applications.
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PrismML's Bonsai 27B Brings On-Device AI to Apple iPhone 17 Pro
PrismML has developed Bonsai 27B, a model specifically optimised for on-device inference on Apple's iPhone 17 Pro. This represents a significant step toward practical large-scale LLM deployment on consumer mobile devices.
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Apple's Hardware Is Ready for On-Device AI and PrismML Just Delivered a Real Breakthrough
Apple's latest hardware capabilities combined with PrismML breakthroughs enable practical on-device AI inference, signaling mature support for local LLM deployment on iOS and macOS ecosystems.
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Deploying 1-Bit Bonsai-27B with PrismML and llama.cpp for Local Inference
A new ultra-quantized 1-bit Bonsai-27B model enables efficient local inference using PrismML and llama.cpp with OpenAI-compatible APIs, dramatically reducing memory requirements for on-device deployment.
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Apple in Early Talks With PrismML on AI Compression Tech
Apple explores advanced model compression technology that could enable faster, more efficient on-device AI inference while preserving model quality. Implications for future iPhone and Mac deployments.
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Apple in Talks with PrismML to Shrink AI Models 15x for iPhone Deployment
Apple is exploring partnership with PrismML, a model compression technology that reduces AI model sizes by up to 15x, enabling efficient on-device inference on iPhones. This development signals major progress in making sophisticated language models practical for edge devices.
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Apple Boosts On-Device AI, Partners With PrismML to Enable Running Large Models Locally on iPhone
Apple partners with PrismML to deploy advanced model compression techniques, enabling larger AI models to run efficiently on iPhone hardware without cloud connectivity.
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Apple Explores Running Larger AI Models on iPhone with On-Device Compression
Apple is developing techniques to run significantly larger language models directly on iPhones, including a 27-billion-parameter model for the first time. The company is exploring advanced compression technologies like PrismML to enable this capability.
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Bonsai 1-Bit Models Deliver Exceptional Local Inference Performance
PrismML's Bonsai 1-bit quantization achieves 14x size reduction while maintaining quality, enabling previously impossible deployments on resource-constrained local hardware.
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PrismML Announces 1-Bit Bonsai: First Commercially Viable 1-Bit LLMs
PrismML has released Bonsai-8B, a groundbreaking 1-bit quantised model that fits in just 1.15GB of memory while maintaining competitive performance with Llama 3 8B. This represents a major breakthrough in memory-efficient local LLM deployment, enabling edge inference on severely resource-constrained devices.