{"slug": "ora-smaller-models-same-intelligence", "title": "ORA: Smaller Models. Same Intelligence", "summary": "Ora Computing launched an automated LLM compression engine that reduces model size by up to 70% with minimal accuracy loss, enabling deployment on edge devices, on-prem servers, or cloud infrastructure. The technology cuts GPU costs by over 50% and increases throughput by 4.1x, as demonstrated by compressing Llama 3.1 70B to a 47B model running on a single GPU.", "body_md": "ORA COMPRESSION\n\n# Smaller Models.\n\nSame Intelligence.\n\nAutomated LLM compression that fits your models on any hardware — edge devices, on-prem servers, or cloud — in hours, not months.\n\nFOUNDATION MODEL\n\nHigh Accuracy, Large Size\n\nORA ENGINE\n\nSMALLER MODELS\n\n70% Smaller Size\n\nFOUNDATION MODEL\n\nHigh Accuracy, Large Size\n\nSMALLER MODELS\n\n70% Smaller Size\n\nUp to 70% smaller · 1 GPU instead of 4 · vLLM & llama.cpp native\n\nBENEFITS\n\n## Model Compression for\n\nScalable Performance\n\nStay ahead in AI deployment by using model compression to optimize efficiency, reduce costs, and scale seamlessly.\n\nMemory Footprint\n\nReduce memory footprint by up to 70%. Run larger models on smaller hardware without sacrificing capability.\n\nMinimal Accuracy Loss\n\nControl accuracy loss for your needs. Our information theory-based approach preserves model quality at extreme compression ratios.\n\nReal Savings\n\nCut GPU bills sustainably by over 50%. Smaller models mean lower inference costs — at every scale.\n\nNovel Compression Algorithm\n\nInformation theory-based compression that goes beyond pruning and quantization — achieving unprecedented compression ratios.\n\nLLM Compatible\n\nWorks with the latest large language models including Llama, Mistral, Qwen, SAM 3 and more. Bring your own model.\n\nProduction Ready\n\nCompressed models ready for immediate deployment. Available on Hugging Face with benchmarks and evaluation results.\n\nMIXED QUANTIZATION\n\n## 19.3 GB → 5.7 GB.\n\nSame accuracy.\n\nCompress Qwen 3.5 9B from 19.3 GB to 5.7 GB in 3.9-bit format — without sacrificing benchmark accuracy. Up to 70% smaller memory footprint.\n\n- Up to 70% smaller memory footprint\n- Higher benchmark performance than open-source equivalents\n- Deploy with vLLM or llama.cpp\n\nPARAMETER PRUNING\n\n## 4.1x throughput.\n\n1 GPU instead of 4.\n\nPrune Llama 3.1 70B to ORA-Llama 47B — 30% fewer parameters, runs on a single GPU with 4.1x higher throughput and 72% lower cost per token.\n\n- 30% fewer parameters, 66% lower memory footprint with quantization\n- Maintains Llama 70B benchmark performance on MMLU, Humaneval, MBPP, Arc-Challenge, GSM8K\n- 72% lower cost per token vs Llama 3.1 70B on 4 GPUs\n\n## Numbers that speak for themselves\n\nWHO WE BUILD FOR\n\n## One engine. Four markets.\n\nThe same compression pipeline unlocks value across the entire AI stack — from the silicon up to the cloud.\n\nSilicon Vendors\n\nMake your silicon punch above its memory budget. Fit larger, more capable models inside fixed on-chip memory and NPU precision modes — unlocking use cases your hardware couldn't run before.\n\nEnterprise AI\n\nCut inference cost without giving up accuracy. Compress your fine-tuned, proprietary models to slash cost-per-token and latency — no retraining, deployed in hours, not weeks.\n\nOEMs\n\nCapable AI on-device, within your power and thermal envelope. Deploy multimodal models on hardware you already ship — in-cabin, consumer, industrial — without cloud dependency or added BOM cost.\n\nCloud Providers\n\nMore tokens per GPU, higher margin per rack. Raise serving throughput and pack more concurrent models onto your existing fleet — improving inference economics and sovereign offerings.\n\n## Start Your Journey\n\nwith Ora Today\n\nBegin your journey with Ora Computing today and discover how our solutions can enhance your AI efficiency.", "url": "https://wpnews.pro/news/ora-smaller-models-same-intelligence", "canonical_source": "https://www.oracomputing.com/", "published_at": "2026-06-25 09:50:14+00:00", "updated_at": "2026-06-25 10:14:19.489089+00:00", "lang": "en", "topics": ["large-language-models", "ai-infrastructure", "ai-tools", "ai-products", "ai-startups"], "entities": ["Ora Computing", "Llama", "Mistral", "Qwen", "Hugging Face", "vLLM", "llama.cpp"], "alternates": {"html": "https://wpnews.pro/news/ora-smaller-models-same-intelligence", "markdown": "https://wpnews.pro/news/ora-smaller-models-same-intelligence.md", "text": "https://wpnews.pro/news/ora-smaller-models-same-intelligence.txt", "jsonld": "https://wpnews.pro/news/ora-smaller-models-same-intelligence.jsonld"}}