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Supercharged inference for open‑weight models

More parameters
Models that needed 24-32 GB in FP16 can run with 8-12 GB. You can load a much larger model on the same device.
Up to
4x
lower total memory usage vs. FP16
More speed
Lower memory bandwidth pressure as fewer bits moved per parameter,
especially on bandwidth-limited architectures.
Up to
10x
inference speedup with custom kernel
More autonomy
Arithmetic energy usage is cut by magnitude. Additions of ternary weights instead of floating-point multiplications.
Up to
30x
less energy consumed vs. FP32
Run Larger Models.
Pay Less. Keep Your Data.
Slash inference costs, maintain privacy, and run larger models without scaling hardware.
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What makes QNTY.FI different?
  • Quantization stack
    Your models are time smaller with just a <2% precision loss. A perfect balance between speed & precision.
  • Not only weights
    “podman up” ready containers + API server + benchmarking. Save your MLOps up to 5 working days deploying, tuning and integrating.
  • Bring your model
    Choose from the catalog of pre-quantized models or optimize your own. Tailored to your domain data, safety filters, rare classes.