Model Lab
Model Lab
Research and tools for model optimization — from MoE architectures to weight compression for production inference.
Model Research
Optimization for Production Inference
The Model Lab focuses on bridging the gap between model research and production deployment. We investigate MoE architectures, weight optimization techniques, and inference strategies that reduce cost without sacrificing quality.
MoE Architectures
Research on Mixture of Experts models and their implications for inference economics, routing efficiency, and production deployment.
Weight Optimization
Techniques for model compression, quantization, and pruning that maintain quality while reducing inference costs.
Inference Optimization
Research on MoE models, weight optimization, and techniques for efficient AI deployment at scale.
Papers & Reports
Related Publications
PeakWeights: Weight Optimization Techniques for Efficient Model Deployment
Kalmantic Labs
Research on weight optimization techniques for efficient model deployment, bridging the gap between AI research and production systems.
Inference Optimization and MoE Models for Production Systems
Kalmantic Labs
Deep research into inference optimization strategies, Mixture of Experts model architectures, and their practical implications for AI safety, AI harness design, and autonomous agent deployment.
PeakWeights
Weight Optimization Library
Our open-source weight optimization library for efficient model deployment. Research-backed techniques for model compression, quantization, and inference optimization.
View on GitHubLet's Build Together
Help shape the future of production AI
We publish research openly, build tools for the community, and collaborate with organizations solving real production AI challenges.