Hardware Lab
Hardware Lab
Infrastructure research and optimization for production AI inference systems.
Hardware Research
Infrastructure for Production AI
The Hardware Lab investigates the infrastructure layer of AI inference. From GPU optimization and deployment patterns to cost modeling and memory systems — we research what it takes to run AI efficiently at scale.
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.
Hardware benchmarks and tooling are in development
We're building infrastructure benchmarks that measure real-world hardware performance for AI inference. GPU utilization, memory efficiency, and cost-per-token across different deployment configurations.
Let'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.