We design, deploy, and operate high-performance compute infrastructure for AI research and production — from GPU clusters to distributed inference networks.
DeepScale Intelligence was founded to close the gap between AI research and production-grade infrastructure. Selected for the NSF I-Corps program, we've spent four years engineering systems that make advanced AI deployment practical, reliable, and scalable.
Every deployment starts with research requirements and ends with production SLAs. Our systems are built to survive the transition from prototype to product.
We operate multi-node inference networks with tensor parallelism, load balancing, and model routing — delivering low-latency inference across heterogeneous hardware.
From bare-metal provisioning to Kubernetes orchestration, we design clusters optimized for specific workloads — LLM serving, fine-tuning, embedding pipelines, and multi-modal models.
Working with university research groups and early-stage AI labs to build the infrastructure that turns experimental models into deployed systems.
End-to-end infrastructure engineering for organizations that need AI compute they can count on.
Full lifecycle — hardware selection, rack layout, network fabric, OS provisioning, driver tuning, and orchestration setup. Multi-GPU, multi-node, multi-vendor.
Low-latency model serving across geographically distributed nodes. Tensor parallelism, continuous batching, model routing, and fallback architecture.
Automated pipelines for model training, evaluation, deployment, and monitoring. Version-controlled infrastructure with GitHub Actions, Terraform, and GitOps practices.
Complete on-premise LLM serving stacks with API compatibility, monitoring, authentication, and cost tracking. No cloud egress fees, full data control.
Prometheus, Grafana, structured logging, and custom dashboards for GPU utilization, inference latency, throughput, and model drift detection.
Research environments with reproducible builds (Nix, Docker), experiment tracking (W&B, MLflow), dataset management, and automated evaluation pipelines.
Key engagements and deployments that demonstrate our approach to building reliable AI infrastructure.
Every deployment is an exercise in trade-offs. We prioritize systems that survive contact with production — reproducible builds, observable runtimes, and architectures that degrade gracefully.
Nix, Docker, and Terraform ensure every environment is a first-class artifact — build once, deploy anywhere, no drift.
Monitoring isn't an afterthought. Every deployment ships with structured logs, metrics, and dashboards before the first request hits production.
We optimize for the workloads that matter — not theoretical benchmarks. Real inference latency, real throughput, real reliability under load.
We work with AI labs, research groups, and engineering teams that need infrastructure that works.
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