Our focus areas
The InferGrove research team works on problems at the intersection of systems engineering and machine learning, with a focus on making model deployment practical and efficient.
Cold start optimization
Reducing time-to-first-prediction through intelligent model caching, speculative loading, and container optimization.
GPU scheduling
Efficient multi-tenant GPU scheduling that maximizes utilization while maintaining low latency guarantees.
Model compression
Quantization and distillation techniques that reduce model size without meaningful quality degradation.
Distributed inference
Splitting large models across multiple GPUs and nodes for efficient inference of 100B+ parameter models.
Publications
- Speculative Model Loading for Sub-Second Cold Starts — InferGrove Research, 2026
- Adaptive GPU Scheduling in Multi-Tenant Inference Clusters — InferGrove Research, 2026
- Efficient Batching Strategies for Heterogeneous Model Workloads — InferGrove Research, 2026
Open source
We believe in giving back to the community. Our model packaging tool, Cog, is fully open source and used by thousands of developers to containerize and deploy ML models.