Build & Optimize GPU Infrastructure for AI Training
Our Services
Distributed Training Optimization
Multi-node training running slow? We diagnose and fix network bottlenecks, tune NCCL, configure RDMA, and optimize collective communications.
- NCCL tuning & debugging
- RDMA/RoCE configuration
- InfiniBand optimization
- GPUDirect RDMA setup
- Network topology analysis
GPU Cluster Architecture
Building a new GPU cluster? We design and implement end-to-end infrastructure for AI workloads—on-prem, colo, or cloud.
- Hardware selection & network fabric
- Storage architecture
- Orchestration setup (K8s/Slurm)
- Multi-tenant GPU-as-a-Service
- Billing, metering & isolation
GPU Sharing & Multi-tenancy
GPUs sitting idle while teams wait? We implement proper sharing with isolation—MIG, time-slicing, quotas—so you get 70%+ utilization.
- MIG partitioning & time-slicing
- Kubernetes GPU operators
- Quota management & fair scheduling
- Self-service portals & templates
- Cost allocation & chargeback
GPU Networking & RDMA
Network killing your training throughput? We design and implement RDMA fabrics — InfiniBand, RoCE, GPUDirect — that run at wire rate.
- InfiniBand & RoCE fabric design
- GPUDirect RDMA setup
- Switch configuration & QoS
- Network Operator on Kubernetes
- Dual-network architectures
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.
- DCGM metrics setup
- GPU health monitoring
- Alerting & dashboards
- Fault detection
- Automated recovery
How We Work
We're hands-on engineers, not slide-deck consultants. Here's our process.
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.
Technologies We Work With
GPUs
Networking
Orchestration
Training Frameworks
Frequently Asked Questions
What does BaaZ do?
BaaZ is a specialist GPU infrastructure consultancy. We help AI startups, SMEs, and GPU cloud providers design, build, optimize, and operate GPU clusters — covering distributed training optimization, Kubernetes GPU operations, RDMA networking, observability, multi-tenancy, and full AI-factory greenfield builds.
Who do you typically work with?
Our clients are usually AI-first startups scaling from a handful to hundreds of GPUs, SMEs standing up in-house ML training clusters, and colo/GPU-cloud providers building multi-tenant GPU-as-a-service platforms. Engineering-led teams with concrete bottlenecks or timelines get the most out of the engagement.
Do you work with on-prem, colo, and cloud GPU clusters?
Yes. We've shipped on bare-metal on-prem, colo, managed Kubernetes (EKS, GKE, AKS) and cloud GPU instances.
How are BaaZ engagements typically structured?
Most engagements follow Assess → Diagnose → Implement → Transfer: we audit your existing setup or design, identify real bottlenecks, implement changes hands-on (code, configs, IaC), and document so your team can operate the result. Engagements range from a focused 2-week diagnostic to multi-month greenfield build-and-operate work.
Can you help with an urgent production issue?
Yes. A large fraction of our work is forensic: NCCL timeouts, distributed training that won't scale, GPU jobs failing at 2am. If you're actively on fire, schedule a call and we'll scope a rapid-response engagement.
How do I start working with BaaZ?
Schedule a call at https://cal.com/baazhq. We'll spend the first call understanding what you're trying to do and whether we're the right fit — no sales pitch. If it's a fit, we scope an engagement and start; if it isn't, we'll point you at resources or partners who are.
Ready to Optimize Your GPU Infrastructure?
Let's discuss your challenges. No sales pitch—just a conversation about what you're trying to do and whether we can help.
Schedule a Call