GPU infrastructure · software layer
The software layer between your GPUs and your AI workloads.
You have the hardware, in your own data centre, a colo, or a dedicated cloud. We bring it up, configure the network fabric, put Kubernetes or Slurm on it, and keep it running. Hands-on engineers and upstream contributors to the NVIDIA stack.
Where we work
Everything between installed hardware and a running job.
We start where the hardware is installed. We take what's there, make it run, and keep it running.
Your workloads
Training runs, inference services, notebooks and internal platforms, owned by your ML and product teams.
BaaZ works here
01Bring-up & OSBMC discovery, provisioning, drivers, firmware baselines
02Network fabricRoCE / InfiniBand, GPUDirect RDMA, switch config
03OrchestrationKubernetes with GPU Operator, or Slurm
04Scheduling & sharingKAI Scheduler, MIG, quotas, multi-tenancy
05ObservabilityDCGM, XID detection, dashboards, alerting
06Day-2 operationsUpgrades, fault recovery, capacity, runbooks
Your hardware
GPU servers, switches, storage, power and cooling, supplied and installed by you, your OEM, or your data-centre partner.
On-prem, colo, or dedicated cloud. NVIDIA and AMD GPUs.See all services
New cluster
New GPU hardware arriving?
From racked servers to first training job: BMC discovery, automated OS provisioning, RoCE/RDMA fabric, Kubernetes or Slurm, verified GPUDirect. Done once, done right, handed over with runbooks.
Existing cluster
GPUs underperforming?
Low utilization, slow multi-node training, jobs failing overnight. Usually it's the network, the scheduler, or a config nobody checked. A fixed-scope, two-week audit finds the real bottlenecks and ships the safe fixes.
Services
Build, optimize, and operate GPU infrastructure for AI.
Six ways we engage. Every one is delivered by the engineers you talk to on the first call.
GPU Cluster Architecture
Building a new GPU cluster? Full bring-up on installed hardware: provisioning, fabric, storage integration, orchestration, monitoring.
Learn moreDistributed Training Optimization
Multi-node training running slow? We diagnose and fix network bottlenecks, tune NCCL, configure RDMA, and optimize collective communication.
Learn moreGPU Networking & RDMA
Network killing your training throughput? RDMA fabrics (InfiniBand, RoCE, GPUDirect) configured and verified at wire rate.
Learn moreGPU Sharing & Multi-tenancy
GPUs sitting idle while teams wait? Proper sharing with isolation (MIG, time-slicing, quotas, KAI Scheduler) so installed GPUs get used.
Learn moreGPU Observability & Reliability
Jobs failing at 2am with no visibility? Monitoring that catches GPU failures before jobs crash, and systems that recover automatically.
Learn moreLLM Inference Optimization
Serving stack selection, batching and KV-cache tuning, latency SLO engineering, and cost-per-token analysis on your own GPUs.
Learn moreOpen source & credentials
We build the stack we run.
Our engineers contribute upstream to NVIDIA's GPU and networking projects. Every line below links to the real pull request.
ProjectPull requestStatusDate
KAI SchedulerNVIDIA#857feat(queue-controller): add queue validatormergedMar 2026KAI SchedulerNVIDIA#1382feat: reservation security contextmergedApr 2026Network OperatorNVIDIA#2035feat: RDMA, SR-IOV, Multus auto-restart pods on config changesmergedJan 2026Network OperatorNVIDIA#2070feat: add global config support for NicClusterPolicymergedMar 2026Network OperatorNVIDIA#3147feat: add MTU support to IPoIBNetwork CRDopenDOCA Driver BuildNVIDIA#299feat: install NFS userspace tools on host when ENABLE_NFSRDMA is enabledmergedSep 2026ipoib-cniNVIDIA / Mellanox#132feat: add MTU supportmergedJun 2026Common problems
Most GPU infrastructure is underutilized, overcomplicated, or both.
GPU problems are often not GPU problems. It's the network, the storage, the scheduler, or the config nobody touched since the cluster went live.
| You say | We do | |
|---|---|---|
| 01 | Our GPUs sit idle while teams wait for access | GPU sharing with proper isolation: MIG, time-slicing, quotas, queue-based scheduling |
| 02 | Training is slow on multiple nodes | Network fabric tuning, NCCL optimization, topology and RDMA path fixes |
| 03 | We don't know what's happening in our cluster | Monitoring, alerting, and visibility into GPU health with DCGM, Prometheus and Grafana |
| 04 | Jobs fail randomly and we can't debug them | Logging, XID error detection, fault tolerance, and automated recovery |
| 05 | ML teams wait days for infrastructure tickets | Self-service platforms with guardrails: namespaces, quotas, JupyterLab, golden images |
| 06 | We're building a GPU cloud and don't know where to start | Platform-layer architecture and implementation: scheduling, isolation, monitoring, metering |
How we work
Hands-on engineers. Results, not decks.
We're not a big consultancy that sends you a deck and disappears. We've built this infrastructure ourselves, at startups, in production, under pressure.
01
Assess
We look at your actual metrics, configs, and problems. No assumptions.
02
Diagnose
We find the real bottlenecks. Often it's the network, not the GPUs.
03
Implement
We write code, change configs, tune systems. You see results, not slide decks.
04
Transfer
We document everything so your team can operate it independently.
Who we help
Three kinds of teams call us.
Teams that own GPUs
We bought the hardware. Now it has to earn its keep.
Startups, enterprises and GCCs with GPU servers on-prem, in a colo, or in a dedicated cloud. They're building something new, or getting more out of what's installed.
ServicesChannel partners
Our customer needs the software stack on the boxes we sold.
Hardware resellers, system integrators, GPU cloud and colo providers who need delivery capacity for the layer between the metal and the workloads.
Working with partnersIn-house inference teams
We serve models on our own GPUs and the numbers don't add up.
Teams running LLM inference on their own hardware who need the right serving stack, batching, latency SLOs and a sane cost per token.
Inference optimizationLet's talk
Tell us about your cluster.
No sales pitch. Just a conversation about what you're trying to do and whether we can help, with the engineer who would do the work.