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GPU Infrastructure Consulting

Build & Optimize GPU Infrastructure for AI Training

8.5xFaster Training
70%+GPU Utilization
10xLatency Reduction
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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 guidance
  • Network fabric design
  • Storage architecture
  • Orchestration setup (K8s/Slurm)
  • Monitoring & observability

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 configuration
  • Kubernetes GPU operators
  • Quota management
  • Fair scheduling

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

Self-Service GPU Platform

ML teams waiting days for infrastructure tickets? We build self-service platforms with guardrails so teams can provision GPU environments themselves.

  • Self-service portals
  • Resource quotas & limits
  • Environment templates
  • Access control
  • Cost allocation

GPU Cloud Platform

Building GPU-as-a-service for customers? We help startups and colo providers build the platform layer—scheduling, isolation, monitoring, billing.

  • Multi-tenant architecture
  • Billing integration
  • Customer isolation
  • API design
  • Usage metering

How We Work

We're hands-on engineers, not slide-deck consultants. Here's our process.

1

Assess

We look at your actual metrics, configs, and problems. No assumptions.

2

Diagnose

We find the real bottlenecks—often it's the network, not the GPUs.

3

Implement

We write code, change configs, tune systems. You see results, not decks.

4

Transfer

We document everything so your team can operate it independently.

Technologies We Work With

GPUs

H100A100L40SA6000V100

Networking

InfiniBandRoCERDMAGPUDirectNCCL

Orchestration

KubernetesSlurmGPU OperatorNetwork Operator

Training Frameworks

PyTorch DDPDeepSpeedMegatronFSDP
Case Study

8.5x Faster Distributed Training with RDMA

How we helped a computer vision company achieve 10x latency improvement with GPUDirect RDMA over RoCE on bare metal Kubernetes.

Read the full case study →
8.5xFaster Training
10xLatency Reduction

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