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Smaller models. Same accuracy.
Quantization-aware training (QAT) lets you deploy models at INT4 or INT8 precision without accuracy loss. Models run 2-4x faster, use 75% less memory, and cost significantly less to serve — all while maintaining your accuracy benchmarks.
Production-ready quantization with no accuracy regression.
Post-training quantization
We apply PTQ to your existing model for immediate size and speed gains — a fast path to smaller models with minimal accuracy impact.
Quantization-aware fine-tuning
When PTQ isn't enough, we integrate fake quantization nodes into the training graph. Your model learns to compensate for reduced precision during training, not after.
Mixed-precision deployment
Not all layers need the same precision. We profile your model and assign optimal bit widths per layer — INT8 for robust layers, INT4 for the rest.
Hardware-specific quantization
Target-specific quantization for NVIDIA, AMD, Apple Silicon, Qualcomm, or edge NPUs. We optimize the precision scheme for your deployment hardware.
Accuracy validation suite
We build a comprehensive eval suite before and after quantization. You get a report showing per-metric accuracy impact before you deploy.
Serving infrastructure
Deploy your quantized model with vLLM, TGI, or ONNX Runtime. We help you set up the serving stack for maximum throughput and minimum latency.