Build, optimize, and operate GPU infrastructure for AI.
From full cluster bring-up (provisioning, network fabric, orchestration, scheduling) to training performance, inference platforms, and Day-2 operations. All of it on hardware you already own or rent.
GPU Cluster Architecture
Building a new GPU cluster? We bring up the full software stack on installed hardware, on-prem, colo, or dedicated cloud, so the first training job runs on a cluster that is already production-grade.
- BMC discovery, automated OS provisioning, driver and firmware baselines
- RoCE or InfiniBand fabric configuration with verified GPUDirect RDMA
- Kubernetes with GPU Operator and KAI Scheduler, or Slurm
- Storage integration, monitoring, runbooks and handover
Distributed Training Optimization
Multi-node training running slow? We diagnose and fix network bottlenecks, tune NCCL, configure RDMA, and optimize collective communication, measured with nccl-tests before and after.
- NCCL tuning, RDMA/RoCE configuration, InfiniBand optimization
- Topology-aware placement: NVLink, PCIe, NIC-to-GPU affinity
- PyTorch DDP / FSDP, DeepSpeed, Megatron communication patterns
- Checkpoint and data-loading path review
GPU Networking & RDMA
Network killing your training throughput? We configure and verify RDMA fabrics (InfiniBand, RoCE, GPUDirect) so they run at wire rate, with the switch-side settings to match.
- PFC/ECN, DCB, MTU and GID index configuration for RoCE
- Secondary networks in Kubernetes: Multus, SR-IOV, host-device, IPoIB
- NVIDIA Network Operator, DOCA/MOFED drivers
- Fabric validation with ib_write_bw, nccl-tests and NCCL debug traces
GPU Sharing & Multi-tenancy
GPUs sitting idle while teams wait? We implement proper sharing with isolation (MIG, time-slicing, quotas, queue-based scheduling) so installed GPUs get used and teams stop queueing behind each other.
- KAI Scheduler queues, quotas, gang scheduling and preemption
- MIG profiles, time-slicing and fractional GPU requests
- Namespaces, guardrails and self-service JupyterLab environments
- Usage metering for chargeback or showback
GPU Observability & Reliability
Jobs failing at 2am with no visibility? We build monitoring that catches GPU failures before jobs crash, and systems that recover automatically, plus the runbooks your team needs to operate it.
- DCGM exporter, Prometheus and Grafana dashboards for GPU health
- XID error detection, node cordoning and automated recovery
- Alerting tied to job impact, not just node metrics
- Capacity planning and Day-2 runbooks
LLM Inference Optimization
Serving models on your own GPUs? We select and tune the serving stack for your latency and cost targets, and make the platform underneath it boring.
- Serving stack selection: vLLM, TensorRT-LLM, SGLang, llm-d, Triton
- Batching, KV-cache and quantization tuning against latency SLOs
- Multi-model routing, autoscaling and GPU packing on Kubernetes
- Cost-per-token analysis and capacity sizing
Start small. Scale the engagement to the problem.
Every engagement is fixed-scope and delivered by senior engineers. Most start with an audit or a single deal and grow from there.
GPU Cluster Audit
A fixed-scope, two-week engagement on an existing cluster. We find the real bottlenecks, ship the safe fixes during the audit, and hand over a prioritized plan.
About the auditBuild or fix
Hands-on delivery with a defined outcome: a new cluster brought up, a fabric made to run at wire rate, a scheduler and sharing model put in place. Documented and handed over.
Scope a projectOngoing operations
Retained engineering for teams that want the cluster kept healthy: upgrades, fault recovery, capacity planning, without hiring a full platform team.
Discuss Day-2 supportAssess, diagnose, implement, transfer.
Assess
We look at your actual metrics, configs, and problems. No assumptions.
Diagnose
We find the real bottlenecks. Often it's the network, not the GPUs.
Implement
We write code, change configs, tune systems. You see results, not decks.
Transfer
We document everything so your team can operate it independently.
GPUDirect RDMA over RoCE on bare-metal Kubernetes
What we found in a multi-node training setup running over TCP/IP, what we changed in the fabric, the CNI and NCCL, and how we verified the RDMA path across the fabric.
Not sure which service fits?
Describe the cluster and the problem. We'll tell you on the call whether it's an audit, a project, or not us.