ORA COMPRESSION
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.