GPU infrastructure engineering: the software layer above the hardware
Post-rack, we make your GPU servers run AI workloads reliably — and keep them that way. Hands-on engineers, upstream contributors to the NVIDIA stack.
New GPU hardware arriving?
From racked servers to first training job in weeks: BMC discovery, automated OS provisioning, RoCE/RDMA fabric, Kubernetes or Slurm, verified GPUDirect. Your hardware, your data centre — our software layer.
How we bring a cluster to life →GPUs underperforming?
Low utilization, slow multi-node training, jobs failing overnight - usually the network, the scheduler, or a config nobody checked. The most common cause we find is RDMA hardware that's installed and silently unused — NCCL running over TCP.
Book a GPU Cluster Audit →Open Source & Certifications
We build the stack we run. Our engineers contribute upstream to NVIDIA's GPU and networking projects and hold vendor-verified certifications.
7 pull requests (6 merged) across 4 upstream projects in the NVIDIA GPU and networking stack.
GPU-aware batch scheduler for Kubernetes.
- #857feat(queue-controller): add queue validatorMergedMar 2026
- #1382feat: reservation security contextMergedApr 2026
Manages RDMA, SR-IOV and Multus networking on Kubernetes.
Builds NVIDIA DOCA / MOFED driver containers for Kubernetes.
IP-over-InfiniBand CNI plugin for Kubernetes pods.
- #132feat: add MTU supportMergedJun 2026
Vendor-verified certifications and open-source foundation memberships held across the team.
Why GPU Infrastructure Underperforms
Most GPU infrastructure is underutilized, overcomplicated, or both.
You bought expensive hardware - H100s, A100s, L40s - but:
- Utilization sits at 30-40% while teams wait for access
- Training jobs fail at 2am and nobody knows why
- Your "multi-tenant" setup is really just SSH and hope
- Networking bottlenecks kill distributed training performance
- You're not sure if the problem is hardware, software, or config
Every idle GPU-hour is money burned. Every failed training run is weeks lost. We help you fix that.
GPU Infrastructure Consulting Services
We make installed GPU hardware work as an AI platform — build the stack, optimize it, operate it.
Higher Utilization
Raise low GPU utilization. Share GPUs safely across teams. Run inference by day, training by night. Stop leaving money on the table.
Faster Training
Eliminate network bottlenecks. Fix PCIe topology issues. Tune collective communications. Get your training jobs finishing in days, not weeks.
Reliable Operations
Know when GPUs are failing before jobs crash. Get visibility into what's actually happening. Build systems that recover automatically.
Self-Service Access
Let your ML teams provision GPU environments themselves - with guardrails. No more tickets. No more waiting. Ship faster.
Lower Costs
Delay your next hardware purchase by getting more from what you have. Or build new infrastructure right the first time.
Hands-On Implementation, Not Slide Decks
We're not a big consultancy that sends you a deck and disappears. We're hands-on engineers who've run this software layer ourselves - at startups, in production, under pressure. We work forward-deployed: embedded in your environment, shipping code and configs alongside your team until the cluster runs.
Understand Your Situation
We start by understanding what you have, what's working, and what's not. No assumptions. We look at the actual metrics, the actual configs, the actual problems.
Identify the Bottlenecks
GPU problems are often not GPU problems. It's the network. It's the storage. It's the scheduler. It's the config nobody touched since 2022. We find the real issues.
Fix What Matters
We implement solutions - not recommendations. We write code, change configs, tune systems. You see results, not slide decks.
Transfer Knowledge
We don't want you dependent on us forever. We document what we did and why, and make sure your team can operate it going forward.
Common Problems We Solve
| You Say | We Do |
|---|---|
| "Our GPUs sit idle while teams wait for access" | MIG partitioning, time-slicing, Kubernetes GPU operators, quota management |
| "Distributed training is slow on multiple nodes" | NCCL optimization, RDMA configuration, InfiniBand/RoCE tuning |
| "We don't know what's happening in our cluster" | Monitoring, alerting, and visibility into GPU health |
| "Jobs fail randomly and we can't debug them" | Logging, fault tolerance, and automated recovery |
| "ML teams wait days for infrastructure tickets" | Self-service platforms with guardrails |
| "We're building a GPU cloud and don't know where to start" | Platform-layer architecture and implementation — scheduling, isolation, monitoring, billing integration |
| "Hardware arrived weeks ago and it's still not provisioned" | BMC enrollment, MaaS/PXE provisioning, firmware baseline, repeatable node builds |
| "We're serving LLMs on dedicated GPUs and the bill doesn't match the throughput" | Inference stack selection and tuning - batching, KV-cache, parallelism, autoscaling |
GPU Infrastructure for Startups and Scale-Ups
"Our GPU hardware is arriving and nobody's set it up before"
The servers are ordered or racked. You want the software layer right the first time without spending months on what NVIDIA's docs don't tell you.
"We're building a GPU cloud for customers"
You're a startup or colo provider building GPU-as-a-service. You need the platform layer - scheduling, isolation, monitoring, billing integration.
"We bought GPUs but they're sitting underutilized"
You invested in hardware but only a few people can use it. Utilization reports look bad. Leadership is asking questions.
"Our training jobs are slow and we don't know why"
Multi-node training should be faster. Something's wrong with the network, the topology, the collective comms - but you can't pinpoint it.
Featured Resources
Technical deep-dives from our work in GPU infrastructure.
GPUDirect RDMA over RoCE on a 2-node bare-metal Kubernetes cluster
How we moved a 2-node, 4-GPU training cluster from 1GbE TCP to 100GbE RoCE, and the configuration that made it real.
Read case study →BlogHow to Calculate if Your Network is Bottlenecking Distributed Training
A practical guide to understanding why your multi-node GPU training might be slower than expected.
Read article →BlogGPU to GPU Communication Across Nodes
Understanding how GPUs communicate in distributed training setups.
Read article →We also work through partners — hardware resellers, system integrators, colo and GPU cloud providers — as the software layer on the hardware they sell. Partners →
Let's Talk
If you're dealing with GPU infrastructure challenges - utilization, performance, reliability, or building something new - we should talk.
No sales pitch. Just a conversation about what you're trying to do and whether we can help.
Schedule a Call