GPU infrastructure from rack to running workloads
We build, optimize, and operate GPU clusters for teams that run their own hardware - on-prem, colo, or dedicated cloud. Hands-on engineers, upstream contributors to the NVIDIA stack.
Standing up new GPU infrastructure?
From hardware delivery to first training job in weeks: BMC discovery, automated OS provisioning, RoCE/RDMA fabric, Kubernetes or Slurm, verified GPUDirect.
How we deliver a cluster →GPUs underperforming?
Low utilization, slow multi-node training, jobs failing overnight - usually the network, the scheduler, or a config nobody checked. In one customer's cluster, fixing the RDMA path made distributed training 8.5x faster.
Book a GPU Cluster Audit →Technical Credentials
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 help companies get the most out of their GPU infrastructure.
Higher Utilization
Turn 30% utilization into 70%+. 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 built this infrastructure 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" | End-to-end architecture and implementation |
| "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
"We need to build GPU infrastructure from scratch"
You're standing up a new AI cluster - on-prem, colo, or cloud. You want to get it right the first time without spending months figuring out 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.
8.5x Faster Distributed Training: RDMA on Bare Metal Kubernetes
How we helped a computer vision company achieve 10x latency improvement with GPUDirect RDMA over RoCE.
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 take delivery work through partners - hardware resellers, system integrators, and GPU cloud providers. 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