# ORA: Smaller Models. Same Intelligence > Source: > Published: 2026-06-25 09:50:14+00:00 ORA COMPRESSION # Smaller Models. Same Intelligence. Automated LLM compression that fits your models on any hardware — edge devices, on-prem servers, or cloud — in hours, not months. FOUNDATION MODEL High Accuracy, Large Size ORA ENGINE SMALLER MODELS 70% Smaller Size FOUNDATION MODEL High Accuracy, Large Size SMALLER MODELS 70% Smaller Size Up to 70% smaller · 1 GPU instead of 4 · vLLM & llama.cpp native BENEFITS ## Model Compression for Scalable Performance Stay ahead in AI deployment by using model compression to optimize efficiency, reduce costs, and scale seamlessly. Memory Footprint Reduce memory footprint by up to 70%. Run larger models on smaller hardware without sacrificing capability. Minimal Accuracy Loss Control accuracy loss for your needs. Our information theory-based approach preserves model quality at extreme compression ratios. Real Savings Cut GPU bills sustainably by over 50%. Smaller models mean lower inference costs — at every scale. Novel Compression Algorithm Information theory-based compression that goes beyond pruning and quantization — achieving unprecedented compression ratios. LLM Compatible Works with the latest large language models including Llama, Mistral, Qwen, SAM 3 and more. Bring your own model. Production Ready Compressed models ready for immediate deployment. Available on Hugging Face with benchmarks and evaluation results. MIXED QUANTIZATION ## 19.3 GB → 5.7 GB. Same accuracy. Compress 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. - Up to 70% smaller memory footprint - Higher benchmark performance than open-source equivalents - Deploy with vLLM or llama.cpp PARAMETER PRUNING ## 4.1x throughput. 1 GPU instead of 4. Prune 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. - 30% fewer parameters, 66% lower memory footprint with quantization - Maintains Llama 70B benchmark performance on MMLU, Humaneval, MBPP, Arc-Challenge, GSM8K - 72% lower cost per token vs Llama 3.1 70B on 4 GPUs ## Numbers that speak for themselves WHO WE BUILD FOR ## One engine. Four markets. The same compression pipeline unlocks value across the entire AI stack — from the silicon up to the cloud. Silicon Vendors Make 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. Enterprise AI Cut 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. OEMs Capable 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. Cloud Providers More 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. ## Start Your Journey with Ora Today Begin your journey with Ora Computing today and discover how our solutions can enhance your AI efficiency.