THE FRAMEWORK
OraCompress:
Full-Stack LLM Optimization
A three-stage automated pipeline that compresses any large language model for any deployment target — edge devices, on-prem servers, or cloud — in hours.
THREE STAGES
Prune. Quantize. Retrain.
OraPrune
Structural Parameter Pruning
Structural pruning that removes redundant parameters while preserving model architecture compatibility. Works on any transformer-based LLM without custom kernels.
0%
Fewer parameters
- Architecture-preserving — no custom inference kernels needed
- Hardware-agnostic model compatible with all runtimes
- Configurable target ratio with accuracy constraints
OraQuant
Mixed-Precision Quantization
Per-layer 1–8 bit precision based on sensitivity analysis. Maximizes compression while preserving accuracy-critical parameters.
0%
Memory reduction
- Per-layer sensitivity analysis for optimal bit assignment
- Produces standard GGUF and llama.cpp-compatible weights
- Supports vLLM and llama.cpp out of the box
OraTrain
Accuracy Recovery Retraining
Fine-tuning that recovers original model accuracy and achieves baseline benchmark performance.
~0%
Vs. baseline accuracy
- Knowledge distillation from the full-precision model
- Targeted retraining on specific damaged model capabilities
- Validated on MMLU-Pro, GPQA-Diamond, AIME-25, LiveCodeBench V6, and BFCL
DEPLOYMENT TARGETS
Deploy Anywhere
OraCompress output is runtime-agnostic. Deploy the same compressed model to any target without re-compressing.
Cloud
- AWS, GCP, Azure-compatible
- vLLM serving with 4× GPU throughput
- Up to 72% lower cloud cost
On-Premise
- Deploy on your own OEM hardware
- Fine-tuned — no higher license cost
- vLLM and llama.cpp supported
Edge
- Fits consumer devices — e.g. 7B → 5 GB
- CPU-driven inference with llama.cpp
- No internet dependency at inference time
Start Your Journey
with Ora Today
Begin your journey with Ora Computing today and discover how our solutions can enhance your AI efficiency.