Hardware Lab
Hardware Lab
Infrastructure research and optimization for production AI inference systems.
Hardware Research
Infrastructure for Production AI
Inference performance is not an ML problem. It is a systems problem. GPU utilization, memory bandwidth, batch scheduling, network topology, and cost modeling determine whether your deployment is viable at scale. We research the infrastructure layer.
Research Focus
Focus Areas
GPU Optimization
Research on GPU utilization patterns, batch scheduling, and compute allocation strategies for inference workloads.
Deployment Patterns
Production deployment architectures for AI inference — from single-node setups to distributed multi-GPU clusters.
Cost Infrastructure
Understanding the true cost of inference hardware. TCO modeling, spot vs. reserved, and hybrid cloud strategies.
Latency Profiling
End-to-end latency analysis across the inference stack — from network ingress to model execution to response delivery.
Network Topology
Optimal network configurations for distributed inference, including interconnect bandwidth and data locality considerations.
Memory Systems
GPU memory management, KV-cache optimization, and memory-efficient serving strategies for large models.
Infrastructure Benchmarks
Measuring real-world hardware performance for AI inference: GPU utilization, memory efficiency, and cost-per-token across deployment configurations. The numbers that actually determine whether your serving setup is viable.
Open Source
Everything we build is public
Benchmarks, tools, and research. All on GitHub. Contributions welcome.