LighterModels.Less Energy.SameIntelligence.
Compress, quantize, and accelerate your AI models with a single API call. Up to 8x smaller, 60% less energy — zero accuracy loss.
Model Compression
Energy Reduction
Accuracy Loss
Avg Latency
Capabilities
Every optimization technique, one API
Stop juggling six different tools. Lumoxic combines quantization, pruning, distillation, and benchmarking into a single platform.
Model Quantization
Reduce model size up to 8x with INT8/INT4 quantization while preserving accuracy within 0.5%.
Neural Pruning
Remove redundant parameters automatically. Structured pruning keeps architecture hardware-friendly.
Knowledge Distillation
Train smaller student models from your large teachers. Same intelligence, fraction of the compute.
Energy Benchmarking
Real-time energy consumption tracking per inference. Know exactly what each prediction costs.
How It Works
Three steps to a lighter model
Upload Your Model
Push any PyTorch, TensorFlow, ONNX, or JAX model through our API or dashboard.
Configure & Optimize
Choose your optimization strategy — quantize, prune, distill, or let our engine pick the best combination.
Deploy Lighter
Get back a production-ready model that's smaller, faster, and cheaper to run. One API call.
Real Results
See the difference
ResNet-50 optimized for edge deployment using INT8 quantization and structured pruning. These are real benchmarks, not estimates.
import lumoxic
# Initialize with your API key
client = lumoxic.Client("lmx_your_key")
# Optimize any model in one call
result = client.optimize(
model="./my_model.onnx",
target="mobile",
strategy="auto"
)
print(result.summary)
# → 83% smaller | 4.2x faster | 0.3% accuracy deltaWorks With Your Stack
Ready to make your models lighter?
Join the beta and start optimizing today. Free tier includes 100 optimizations per month — no credit card required.