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ORA COMPRESSION

Smaller Models.<br>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<br>High Accuracy, Large Size

LlamaQwenMistralGemmaand more.

ORA ENGINE

OraPrune

OraQuant

OraTrain

SMALLER MODELS<br>70% Smaller Size

FOUNDATION MODEL<br>High Accuracy, Large Size

LlamaQwenMistralGemmaand more.

OraPrune

OraQuant

OraTrain

SMALLER MODELS<br>70% Smaller Size

RuntimesCompatible with<br>llama.cppllama.cppLLMvLLM

TargetsEdge<br>Cloud<br>On-Prem

Up to 70% smaller · 1 GPU instead of 4 · vLLM & llama.cpp native

BENEFITS

Model Compression for<br>Scalable Performance

Stay ahead in AI deployment by using model compression to optimize efficiency, reduce costs, and scale seamlessly.

Memory Footprint<br>Reduce memory footprint by up to 70%. Run larger models on smaller hardware without sacrificing capability.

Minimal Accuracy Loss<br>Control accuracy loss for your needs. Our information theory-based approach preserves model quality at extreme compression ratios.

Real Savings<br>Cut GPU bills sustainably by over 50%. Smaller models mean lower inference costs — at every scale.

Novel Compression Algorithm<br>Information theory-based compression that goes beyond pruning and quantization — achieving unprecedented compression ratios.

LLM Compatible<br>Works with the latest large language models including Llama, Mistral, Qwen, SAM 3 and more. Bring your own model.

Production Ready<br>Compressed models ready for immediate deployment. Available on Hugging Face with benchmarks and evaluation results.

MIXED QUANTIZATION<br>19.3 GB → 5.7 GB.<br>Same accuracy.<br>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.<br>Up to 70% smaller memory footprint<br>Higher benchmark performance than open-source equivalents<br>Deploy with vLLM or llama.cpp

PARAMETER PRUNING<br>4.1x throughput.<br>1 GPU instead of 4.<br>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.<br>30% fewer parameters, 66% lower memory footprint with quantization<br>Maintains Llama 70B benchmark performance on MMLU, Humaneval, MBPP, Arc-Challenge, GSM8K<br>72% lower cost per token vs Llama 3.1 70B on 4 GPUs

Numbers that speak for themselves

0%smaller memory footprint

0.0×throughput increase

0%lower cost per token

Hoursto compress & deploy

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.<br>NPUsEdge acceleratorsAutomotive SoCs

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.<br>SaaS platformsFine-tuned LLMsSelf-hosted

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.<br>AutomotiveConsumer devicesIndustrial edge

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.<br>Sovereign cloudInference platformsGPU fleets

Start Your Journey<br>with Ora Today<br>Begin your journey with Ora Computing today and discover how our solutions can enhance your AI efficiency.<br>Contact UsExplore Models →