AI Inference Optimization
We make AI models run from cloud GPUs to local CPUs, from real-time to batch processing, maintaining quality while meeting constraints.
Deployment strategies
MLOps expertise ensures smooth transitions from development and training to production with automated pipelines handling versioning, monitoring, and scaling.
- Multi-Model System deployment and orchestration
- Computational Resource Management and Optimization
- Optimal GPU Utilization
- MLops
Production LLM deployment
Deploy LLMs effectively across environments, from local hosting for data privacy to hybrid architectures balancing cost, performance, and a restrictive data access policy.
Streaming implementations and inference optimization make real-time AI interactions practical even with resource constraints.
- Local hosting – Running 70B parameter models
- Inference optimization
- Cost optimization
- Streaming
- Hybrid local/cloud deployment for cost optimization
- Privacy-preserving inference without data leaving premises
Model selection
Match the right model to each task, avoiding the inefficiency of using oversized models for simple problems or undersized ones for complex challenges.
Our systematic approach evaluates task requirements against model capabilities, creating efficient workflows that dynamically route queries to appropriate models based on complexity and required accuracy.
- Matching model size to task complexity
- Efficient and understandable LLM workflows
- Model switching based on query complexity
