Research that makesAI run better.

We reduce model memory, decoding work, and latency, then bring proven gains into QDivZero.

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How we validate new ideas

Define the production problem.

We begin with a bottleneck observed in a real inference workload and turn it into a measurable validation target.

  • Capture the model, hardware, workload, and traffic profile.
  • Choose the primary metric: memory, latency, or throughput.
  • Set quality and reliability limits before testing.

Test against a controlled baseline.

We run the current runtime and the candidate method under the same conditions, then compare performance and output quality.

  • Repeat runs across representative models and hardware.
  • Measure peak memory, latency, throughput, and output quality.
  • Investigate variance and reject results we cannot reproduce.

Graduate repeatable improvements.

A method reaches QDivZero only when its gains persist across production-like workloads without changing the runtime contract.

  • Confirm the improvement on held-out workloads.
  • Check operational cost, stability, and failure modes.
  • Integrate behind the existing API and monitor after release.

Research that moves inference forward.

Research methods already implemented in the QDivZero platform to make AI systems more efficient.

Quantization

Quantization stores model weights at lower precision to reduce memory use, helping compatible models run on fewer resources without sacrificing quality or response time.

Multi-Token Prediction

Multi-Token Prediction proposes several tokens in each decoding step, reducing sequential work and response time when the model architecture supports it.

Speculative decoding

Speculative decoding lets a smaller model propose token blocks for the main model to verify, accelerating generation without changing the final output.

Pruning strategies

Pruning removes experts or parameters with little impact on output, avoiding unnecessary computation while preserving quality and deployment stability.

Ready to put the research to work?

Follow the methods we are validating or bring QDivZero into a production workload.

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