We help AI teams cut inference costs and carbon emissions by up to 80% — using pruning, sparse attention, and quantization — without sacrificing model accuracy.
$ sparsity optimize ./gpt2-large.pt
Analyzing architecture ................ done
Detecting sparse regions .............. done
Applying structured pruning .......... done
Validating accuracy .................. done
───────────────────────────────────
FLOPs reduced 78.4%
Latency 3.1x faster
Model size -82%
Accuracy delta 0.003%
CO2 saved 41.2 kg
───────────────────────────────────
Output: ./gpt2-large-sparse.pt savedNo ML expertise required. Upload your model and let our engine handle the rest.
Submit a trained PyTorch or TensorFlow model via API or dashboard. We support all major architectures — transformers, CNNs, and recurrent networks.
Our optimizer analyzes weight distributions and applies the right combination of structured pruning, quantization, sparse attention patterns, and mixture-of-experts routing.
Receive your sparsified model alongside a signed Green Report — exact FLOPs saved, energy reduction, carbon equivalent, and accuracy delta. Ready for production.
Upload any trained model and receive an optimized version. The engine applies the right mix of techniques automatically — you get a smaller, faster model without writing a single line of optimization code.
Every optimization generates a signed, verifiable report: FLOPs saved, kWh reduced, CO2 equivalent. Precise metrics your sustainability team can publish or submit to auditors — no estimates.
Deploy sparsified models with hardware-optimized kernels for CPU, GPU, and TPU. Purpose-built for sparse weight matrices — not generic wrappers — so you get the full latency benefit at inference time.
Deploy LLMs on a budget. Cut cloud inference bills without retraining from scratch or writing custom CUDA kernels. One API call, measurably faster model.
Run more experiments with the same grant budget. Reduce compute hours and publish verifiable green compute metrics alongside results — reviewers and funders notice.
Hit net-zero commitments with hard numbers. Signed Green Reports, on-prem deployment options, and SLA-backed support for your production AI stack.
Fit capable models onto constrained hardware. Our sparse kernels are designed for low-power environments — mobile, IoT, embedded — not just cloud-scale inference.
No surprise bills. Transparent pricing at every tier.
Full plan details and pay-per-savings pricing on the pricing page.
Sparsify your first model free. No credit card required.
Create free account